Environ Monit Assess (2017) 189:187 DOI 10.1007/s10661-017-5899-1
Large-scale carbon stock assessment of woody vegetation in tropical dry deciduous forest of Sathanur reserve forest, Eastern Ghats, India Durai Sanjay Gandhi & Somaiah Sundarapandian
Received: 9 December 2016 / Accepted: 14 March 2017 # Springer International Publishing Switzerland 2017
Abstract Tropical dry forests are one of the most widely distributed ecosystems in tropics, which remain neglected in research, especially in the Eastern Ghats. Therefore, the present study was aimed to quantify the carbon storage in woody vegetation (trees and lianas) on large scale (30, 1 ha plots) in the dry deciduous forest of Sathanur reserve forest of Eastern Ghats. Biomass of adult (≥10 cm DBH) trees was estimated by species-specific allometric equations using diameter and wood density of species whereas in juvenile tree population and lianas, their respective general allometric equations were used to estimate the biomass. The fractional value 0.4453 was used to convert dry biomass into carbon in woody vegetation of tropical dry forest. The mean aboveground biomass value of juvenile tree population was 1.86 Mg/ha. The aboveground biomass of adult trees ranged from 64.81 to 624.96 Mg/ha with a mean of 245.90 Mg/ ha. The mean aboveground biomass value of lianas was 7.98 Mg/ha. The total biomass of woody vegetation (adult trees + juvenile population of trees + lianas) ranged from 85.02 to 723.46 Mg/ha, with a mean value of 295.04 Mg/ha. Total carbon accumulated in
D. S. Gandhi : S. Sundarapandian (*) Department of Ecology and Environmental Sciences, Pondicherry University, Puducherry 605014, India e-mail:
[email protected] S. Sundarapandian e-mail:
[email protected]
woody vegetation in tropical dry deciduous forest ranged from 37.86 to 322.16 Mg/ha with a mean value of 131.38 Mg/ha. Adult trees accumulated 94.81% of woody biomass carbon followed by lianas (3.99%) and juvenile population of trees (1.20%). Albizia amara has the greatest biomass and carbon stock (58.31%) among trees except for two plots (24 and 25) where Chloroxylon swietenia contributed more to biomass and carbon stock. Similarly, Albizia amara (52.4%) showed greater carbon storage in juvenile population of trees followed by Chloroxylon swietenia (21.9%). Pterolobium hexapetalum (38.86%) showed a greater accumulation of carbon in liana species followed by Combretum albidum (33.04%). Even though, all the study plots are located within 10 km radius, they show a significant spatial variation among them in terms of biomass and carbon stocks which could be attributed to variation in anthropogenic pressures among the plots as well as to changes in tree density across landscapes. Total basal area of woody vegetation showed a significant positive (R2 = 0.978; P = 0.000) relationship with carbon storage while juvenile tree basal area showed the negative relationship (R2 = 0.4804; P = 0.000) with woody carbon storage. The present study generates a large-scale baseline data of dry deciduous forest carbon stock, which would facilitate carbon stock assessment at a national level as well as to understand its contribution on a global scale.
Keywords Carbon storage . Carbon mitigation . Eastern Ghats . Tropical dry forest . Biomass
187
Environ Monit Assess (2017) 189:187
Page 2 of 18
Introduction Global warming and climate change are perhaps the most pressing global issues in the present scenario. The carbon dioxide (CO2) concentration in the atmosphere has lifted from 277 ppm in 1750 (Joos and Spahni 2008) to 403.64 ppm on November 2016 (NOAA 2016), leading to a subsequent escalation of global temperature. Due to anthropogenic activities such as burning of fossil fuels, deforestation, urbanization, etc., earth’s temperature has significantly risen over the last 50 years that pose great challenges for carbon mitigation strategies, besides socioeconomic, biological problems (Sicard and DalsteinRichier 2015), and origin of new catastrophic diseases (WHO 2015). IPCC in its 4th and 5th assessment reports recommended the member nations to take adequate steps to reduce global warming, which causes severe ecological, social, and economic consequences (Kerr 2007; Sharma et al. 2011; IPCC 2007, 2014). In recent years, all possible steps have been taken by the developed nations in order to plan for carbon mitigation, management, and policy actions. One of the policy measures was Kyoto Protocol which was ratified by most of the industrialized countries to reduce their carbon outputs, carbon taxes, and subsidy systems in support of carbon mitigation targets (Cao et al. 2010). The estimated growing stock of the world’s forests in 2015 was 530.5 billion m3 (Kohl et al. 2015). Under the Kyoto protocol (1997), carbon in a forest ecosystem should be assessed from different pools such as aboveground and belowground biomass, deadwood, litter, and soil. Forests are predominant global carbon sinks as they could restore 70–90% of biomass carbon in terrestrial ecosystems (Houghton et al. 2009; Brienen et al. 2015). Globally, 80% of live forest biomass lies in aboveground and the rest in belowground (Cairns et al. 1997; Jackson et al. 1997). Pan et al. (2011) stated that overall living vegetation biomass accumulated roughly 42% of C, while both soil and litter have 49% and dead wood has 9%. Although tropical forests occupy 15% of the earth’s land surface, they account for two-thirds of terrestrial plant biomass (Pan et al. 2013; Kohl et al. 2015) and one-third of all soil C (Jobbágy and Jackson 2000; Tarnocai et al. 2009). They are known to exchange more CO2 with the atmosphere than any other biome (Foley et al. 2003; Beer et al. 2010). Moreover, these forests contain around 55% of global forest carbon (Pan et al. 2011) and contribute to one-third of gross primary production on land (Beer et al.
2010). Tropical forests contain 247 Gt of carbon in vegetation, and of this, 193 Gt is stored aboveground (Saatchi et al. 2011). Of these tropical forests, dry forests and rainforests contain 110 and 134 Gt of vegetation carbon respectively (Foley 1995). At present, Indian forests stock 7044 million tonnes of carbon (State Forest Report, FSI 2015). In COP 21, Minister of Environment, Forest and Climate Change, India, committed to expansion of the forest cover in order to create additional C sinks of 2.5–3 billion Mg by 2030. The biomass estimations in Indian forests were made by applying allometric equations (species-specific) and component-wise equations, viz., stem, branch, foliage, and root biomass (Rai 1984; Joshi et al. 2015). There are number of reports available on C stocks from dry tropical forest ecosystems in India (Singh and Singh 1991; Singh and Singh 1993; Haripriya 2000; Chhabra et al. 2002; Pande 2005; Mani and Parthasarathy 2007, 2009; Madugundu et al. 2008; Singh et al. 2009; Bijalwan et al. 2010; Chaturvedi et al. 2011a, b, c; Mohanraj et al. 2011; Chaturvedi et al. 2012a; Sundarapandian et al. 2013, 2016; Salunkhe et al. 2014; Chaturvedi and Raghubanshi 2015; Pragasan 2015a, b; Sahu et al. 2016). However, no published information or study is available on biomass and carbon stocks of tropical dry deciduous forest in Sathanur reserve forest of Eastern Ghats. Hence, an attempt was made to assess carbon stocks of woody vegetation in the tropical dry deciduous forest ecosystem of Sathanur reserve forest. Furthermore, an attempt was also made to explore the factors responsible for spatial variation in carbon stock. Study area Sathanur reserve forest (longitude 78° 51′ 10″ and latitude 12° 4′ 48″), a part of Chennakesava hills, Tamil Nadu, India, spread over 870 ha (Fig. 1), was chosen for the present study. The Eastern Ghats experience heavy pressure due to illegal logging, collection of fodder, fuelwood, medicinal plants, etc. and thereby are losing its vegetation at an alarming rate (Jayakumar et al. 2002). According to Champion and Seth (1968), the vegetation of Eastern Ghats is of tropical dry deciduous type. The vegetation in this region is mainly dry deciduous and thorn forests (Type 7/CI of Champion and Seth 1968). Nevertheless, evergreen and semi-evergreen forests are also present in the high altitudes.
Environ Monit Assess (2017) 189:187
Page 3 of 18 187
Fig. 1 Study area map of Sathanur reserve forest of Eastern Ghats, India
Sathanur reserve forest receives a bimodal pattern of rainfall, with maximum rain during northeast monsoon (September–December) and very less and inconsistent rainfall during the southwest monsoon (May to July). The average annual rainfall for 44 years (1972 to 2015) was 965.49 mm, and mean monthly maximum temperatures ranged between 28 and 37 °C while mean monthly minimum temperatures varied from 19.6 to 26.8 °C (Fig. 2). The major soil types in the district are red loam and black soil (Ministry of MoEF, Govt. of India, 2012–2013), and the red loam soil is predominantly found in Sathanur reserve forest (NADP 2008). The texture ranges from sandy loam and gravelly loam at the surface and sandy-clay-loam to gravelly-clay-loam at the subsurface (Sharma 2010).
Biomass and carbon stocks of woody vegetation were assessed using the non-destructive method. The aboveground biomass (AGB) of adult (≥30 cm GBH) tree species was estimated following the allometric equation of Chave et al. (2005). AGB ¼ ρ exp −0:667 þ 1:784 lnðDÞ þ 0:207 ðlnðDÞÞ2 −0:0281 ðlnðDÞÞ3
where, D is the diameter and ρ is the wood specific gravity of tree species. 250
Rain fall(mm)
Max. temp.
Min. temp
40 35
200
Thirty square plots of 1 ha each were laid randomly in the Sathanur reserve forest (Fig. 1) which were further sub-gridded into 10 m × 10 m size (100 m2) quadrats as easy, workable units. All the individual plants with ≥10 cm GBH were enumerated and their girth was measured at 1.37 m from the ground level. Within the plot, 50 quadrats of 5 m × 5 m were laid in a systematic sampling method to enumerate all living liana individuals (≥1 cm GBH) and their diameter was measured at 1.37 m from the rooting point.
25
150
20 100
15
Temperature (°C)
Methods
Rainfall (mm)
30
10 50 5 0
0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month
Fig. 2 Mean monthly rainfall (44 years) and temperature (study period) of the Sathanur reserve forest, Eastern Ghats
187
Environ Monit Assess (2017) 189:187
Page 4 of 18
The wood specific gravity of each tree species was taken from the available published literature (Gisel et al. 1992; Cordero and Kanninen 2002; Mani and Parthasarathy 2007; Wiemann and Green 2007; Sundarapandian et al. 2014a). Belowground biomass (BGB) of trees was calculated following Cairns et al. (1997). BGB ¼ expð−1:0587 þ 0:8836 lnðAGBÞÞ where AGB is aboveground biomass of trees. Aboveground biomass of juvenile tree population (≥10 cm–<30 cm GBH) was estimated using allometric equation following Chaturvedi et al. (2012b). AGB ¼ 3:344 þ 0:443 ln D2
where D is the diameter. Aboveground biomass of lianas (≥1 cm diameter at 1.3 m above the rooting point) was estimated by using the allometric equation given by Schnitzer et al. (2006). AGB ¼ exp ½−1:484 þ 2:657 lnðDÞ where D is the diameter. The belowground biomass of juvenile tree population and lianas was calculated by multiplying the aboveground biomass value with 0.26 (Cairns et al. 1997; IPCC 2003). Carbon stock was determined by the following equation: TCS ¼ ðAGB þ BGBÞ 0:4453 where TCS—Total carbon stock, AGB—aboveground biomass, BGB—belowground biomass and 0.4453 is the conversion factor which represents that the carbon content is assumed as 44.53% of the total biomass for the tropical dry forest (Junior et al. 2016).
Statistical analysis The relationship between biomass and vegetation parameters was examined with regression. Similarly, carbon stock was also correlated with various vegetation parameters and soil characteristics.
Results Aboveground biomass and belowground biomass The total biomass of woody vegetation in tropical dry deciduous forest showed spatial variation among the 30, 1-ha plots (Table 1). The total biomass of woody vegetation in this tropical dry deciduous forest was ranged from 85.02 to 723.46 Mg/ha, with a mean of 295.04 ± 27.64 Mg/ha. The aboveground biomass of woody vegetation across the study plots was ranged from 76.79 to 637.24 Mg/ha, with a mean of 255.74 ± 24.19 Mg/ha. Similarly, the total belowground biomass of woody vegetation ranged from 14.58 to 86.49 Mg/ha with a mean of 39.294 ± 3.35 Mg/ha. The total biomass (AGB + BGB) of adult trees ranged from 76.28 to 708.26 Mg/ha with a mean value 282.64 ± 27.47 Mg/ha, and the biomass of juvenile tree population ranged from 0.49 to 4.25 Mg/ha with a mean value of 2.34 ± 0.18 Mg/ha. The biomass of lianas ranged from 0.49 to 40.33 Mg/ha with a mean of 10.06 ± 1.46 Mg/ha in the tropical dry deciduous forest of Eastern Ghats. Aboveground biomass of adult trees ranged from 64.81 to 624.96 Mg/ha with a mean value of 245.90 ± 24.17 Mg/ha, and juvenile tree population ranged from 0.39 to 3.37 Mg/ha with a mean value of 1.85 ± 0.15 Mg/ha. The belowground biomass of adult trees ranged 11.46 Mg/ha to 83.30 Mg/ha with a mean value of 36.74 ± 3.33 Mg/ha, and belowground biomass of juvenile population of trees ranged 0.10–0.88 Mg/ha with a mean value of 0.48 ± 0.04 Mg/ha. Aboveground biomass of liana community ranged from 0.38 to 32.0 Mg/ha with a mean value of 7.98 ± 1.15 Mg/ha, and belowground biomass of lianas ranged 0.10– 8.32 Mg/ha with a mean value of 2.07 ± 0.30 Mg/ha. Carbon stock The total carbon stock of woody vegetation also showed spatial variation among 30, 1-ha plots in the tropical dry deciduous forest (Table 2). The total woody carbon recorded in the study plots was 3941.37 Mg, and it ranged from 37.86 to 322.16 Mg/ha with a mean value of 131.38 ± 12.31 Mg/ha. Total carbon stock of adult trees in the study plots ranged from 33.97 to 315.39 Mg/ ha with a mean value of 125.86 ± 12.23 Mg/ha, and in juvenile tree population, the values ranged 0.22 Mg/ha– 1.89 Mg/ha with a mean value of 1.04 ± 0.08 Mg/ha,
Environ Monit Assess (2017) 189:187
Page 5 of 18 187
Table 1 Biomass (Mg/ha) of woody vegetation in the tropical dry deciduous forest of Sathanur reserve forest, Eastern Ghats Plot
Tree adult AGB
Tree juvenile BGB
TB
AGB
Liana BGB
TB
AGB
TWB BGB
TB
1
79.7
11.3
91.0
1.3
0.4
1.7
3.5
0.9
4.4
98.9
2
217.8
34.9
252.7
1.8
0.5
2.2
11.6
3.0
14.6
269.5
3
217.7
36.0
253.7
2.7
0.7
3.3
3.0
0.8
3.8
260.8
4
304.9
49.5
354.4
2.1
0.5
2.6
4.3
1.1
5.5
362.4
5
229.6
36.9
266.5
1.9
0.5
2.4
10.9
2.8
13.7
282.7
6
232.7
36.8
269.5
3.0
0.8
3.7
3.0
0.8
3.8
277.0
7
343.3
52.8
396.1
1.8
0.5
2.3
10.5
2.7
13.2
411.6
8
212.0
33.7
245.8
0.4
0.1
0.5
2.9
0.7
3.6
249.8
9
109.4
17.2
126.5
2.2
0.6
2.8
18.8
4.9
23.7
153.0
10
305.8
43.7
349.5
1.2
0.3
1.5
9.5
2.5
12.0
362.9
11
374.8
57.6
432.4
0.7
0.2
0.9
10.6
2.7
13.3
446.6
12
117.8
18.8
136.6
2.4
0.6
3.0
7.2
1.9
9.0
148.7
13
625.0
83.3
708.3
0.6
0.2
0.8
11.5
3.0
14.4
723.5
14
273.3
40.0
313.3
1.3
0.3
1.7
32.0
8.3
40.3
355.2
15
456.0
61.6
517.6
1.3
0.3
1.6
3.7
1.0
4.6
523.9
16
380.0
57.0
437.0
2.6
0.7
3.3
7.8
2.0
9.8
450.1
17
370.9
55.1
426.0
1.1
0.3
1.4
8.7
2.3
11.0
438.4
18
172.2
25.8
198.0
2.2
0.6
2.8
3.5
0.9
4.4
205.1
19
255.3
34.6
289.8
1.3
0.4
1.7
13.8
3.6
17.4
309.0
20
459.8
69.0
528.8
0.7
0.2
0.9
13.6
3.5
17.1
546.9
21
374.7
52.5
427.2
0.7
0.2
0.8
4.5
1.2
5.6
433.6
22
123.8
19.2
143.0
2.3
0.6
2.9
1.4
0.4
1.8
147.8
23
174.4
26.3
200.7
2.3
0.6
2.8
0.4
0.1
0.5
204.1
24
96.9
15.8
112.7
2.6
0.7
3.3
2.0
0.5
2.5
118.5
25
64.8
11.5
76.3
3.3
0.9
4.2
3.7
1.0
4.6
85.0
26
139.6
21.6
161.3
2.3
0.6
2.9
7.4
1.9
9.4
173.5
27
127.2
20.0
147.2
1.8
0.5
2.2
8.7
2.3
11.0
160.4
28
151.6
22.6
174.2
2.4
0.6
3.0
2.5
0.7
3.1
180.4
29
239.0
34.9
273.9
3.4
0.9
4.3
9.9
2.6
12.4
290.6
30
146.9
22.5
169.3
2.2
0.6
2.7
8.9
2.3
11.2
183.3
AGB aboveground biomass, BGB belowground biomass, TB total biomass, TWB total woody biomass
while in lianas, the values ranged from 0.216 to 17.958 Mg/ha with a mean value of 4.478 ± 0.65 Mg/ ha. Adult tree species contributed 94.8% of total recorded woody carbon stock, followed by lianas and juvenile tree population with 3.4 and 1.2% respectively. Among the 75 tree species enumerated, Albizia amara accumulated greater biomass and carbon stocks (58.31%) except in two plots (24 and 25) where Chloroxylon swietenia accumulated more than the other species (Table 3). The top ten species, viz., Albizia amara (58.3), Pongamia pinnata (8.4),
Chloroxylon swietenia (6.8), Syzygium cumini (3.1), Azadirachta indica (2.9), Acacia catechu (2.5), Moringa concanensis (1.6), Diospyros ebenum (1.6), Gyrocarpus jacquinii (1.5), and Albizia lebbeck (1.0), together contributed 87.7% of carbon to the total carbon stocks in the study plots. In contrast, 33 species contributed to only 0.1% of woody carbon stocks. Among lianas, Pterolobium hexapetalum (38.86%) showed a greater accumulation of carbon followed by Combretum albidum (33.04%) and Acacia caesia (12.25%; Table 4).
187
Environ Monit Assess (2017) 189:187
Page 6 of 18
Table 2 Carbon stocks of woody vegetation in the tropical dry deciduous forest of Sathanur reserve forest, Eastern Ghats Plot
Table 3 Carbon stocks (Mg/ha) of adult and juvenile trees in the tropical dry deciduous forest of Sathanur reserve forest, Eastern Ghats
Carbon content (Mg/ha) Tree adult
Tree juvenile
Liana
Name of the species
Adult trees
Juvenile trees
Albizia amara (Roxb.) Bavi
Total woody vegetation
2203.57
16.36
Pongamia pinnata (L.) Pierre
317.88
1.02
120.0
Chloroxylon swietenia DC.
256.19
6.85
1.7
116.2
Syzygium cumini (L.) Skeels
117.10
0.02
1.2
2.4
161.4
Azadirachta indica A.Juss
109.87
0.90
1
40.5
0.8
1.9
43.2
2
112.5
1.0
6.5
3
113.0
1.5
4
157.8
5
118.7
1.1
6.1
125.9
Acacia catechu (L.f.) Willd.
92.85
0.80
6
120.0
1.7
1.7
123.4
Moringa concanensis Nimmo
60.51
0.01
7
176.4
1.0
5.9
183.3
Diospyros ebenum Koen.
59.46
0.39
8
109.4
0.2
1.6
111.3
Gyrocarpus jacquini Roxb.
55.54
0.10
9
56.4
1.2
10.5
68.1
Albizia lebbeck (L.) Benth.
38.12
0.02
10
155.6
0.7
5.3
161.6
Tamarindus indica L.
35.15
0
Ziziphus mauritiana Lam.
33.45
0.18
Canthium dicoccum (Gaertn.) Teys. and Binn. Cassia siamea Lam.
32.53
0.74
31.28
0.14
27.51
0.23
27.06
0.69
26.04
0.09
11
192.6
0.4
5.9
198.9
12
60.8
1.4
4.0
66.2
13
315.4
0.4
6.4
322.2
14
139.5
0.7
18.0
158.2
15
230.5
0.7
2.1
233.3
16
194.6
1.5
4.4
200.5
Dichrostachys cinerea (L.) Wight andArn. Atalantia monophylla (L.) Correa
17
189.7
0.6
4.9
195.2
Prosopis juliflora (Sw.) DC.
18
88.2
1.2
2.0
91.3
Sapindus emarginatus Vahl.
23.93
0.07
19
129.1
0.8
7.8
137.6
Lannea coromandelica (Houtt.) Merr.
23.50
0.02
20
235.5
0.4
7.6
243.5
Wrightia tinctoria (Roxb.) R.Br.
21.63
0.34
21
190.2
0.4
2.5
193.1
Dalbergia paniculata Roxb.
18.19
0.03
22
63.7
1.3
0.8
65.8
Ficus benghalensis L.
16.42
0.04
23
89.4
1.3
0.2
90.9
14.09
0.45
24
50.2
1.5
1.1
52.8
25
34.0
1.9
2.1
37.9
Drypetes sepiaria (W. and A.) PaxandHoffm. Cleistanthus collinus (Roxb.) Benth.
10.40
0.17
9.86
0.27 0.08
26
71.8
1.3
4.2
77.3
Alangium salvifolium (L.f.) Wang.
27
65.6
1.0
4.9
71.4
Diospyros montana Roxb.
9.28
28
77.6
1.3
1.4
80.3
Cassia roxburghii DC.
9.23
0.05
29
122.0
1.9
5.5
129.4
Bauhinia racemosa Lam.
8.62
0.05
30
75.4
1.2
5.0
81.6
Among the 46 woody plant families recorded from the study plots, family Mimosaceae stored the highest woody biomass carbon (2411.45 Mg) and constituted 61.19% of the total woody carbon stock estimated in the tropical dry deciduous forest, followed by Fabaceae (9.22%), Flindersiaceae (6.70%), and Myrtaceae (3.06%; Table 5). A correlation and regression analyses between aboveground biomass and predicting variables and total carbon stock of woody vegetation with predicting
Diospyros ferrea (Willd.) Bakh.
8.29
0.53
Vitex trifolia L.
8.07
0.04
Grewia tiliaefolia Vahl.
7.41
0.01
Mallotus philippensis Muell
5.70
0.04
Terminalia arjuna (Roxb.) Wight and Arn. Delonix regia (Hook.)Raf.
5.67
0.00
5.24
0.01
Acacia leucophloea Roxb.
5.13
0.04
Erythroxylum monogynum Roxb.
4.34
0.05
Holoptelea Integrifolia (Roxb).Planch.
3.62
0
Eucalyptus tereticornis Sm.
3.50
0
Strychnos potatorum L.
2.84
0.03
Delonix elata (L.) Gamble
2.32
0
Environ Monit Assess (2017) 189:187
Page 7 of 18 187
Table 3 (continued) Name of the species
Table 4 Carbon stocks (Mg/ha) of top ten lianas in the tropical dry deciduous forest of Sathanur reserve forest, Eastern Ghats Adult trees
Juvenile trees
Givotia moluccana (L.) Sreem.
2.23
0
Crataeva magna (Lour.) DC.
2.05
0.03
Tricalysia sphaerocarpa (Dalz.) Gamble
2.02
0.00
Borassus flabellifer L.
1.93
0
Strychnos nux-vomica L.
1.46
0.02
Premna serratifolia L.
1.33
0.01
Gmelina asiatica L.
1.32
0.02
Dalbergia sissoo Roxb. ex DC.
1.29
0.05
Dalbergia lanceolaria L.
1.19
0.02
Ixora pavetta Andr.
1.16
0.01
Ailanthus excelsa Roxb.
0.86
0.06
Garcinia spicata (W. and A.) Hook. f.,
0.81
0.03
Kleinhovia hospita L.
0.76
0.00
Acacia nilotica (L.) Willd. ex Delile
0.69
0
Dalbergia oliveri Prain
0.60
0.00
Dolichandrone falcata (Wall. ex DC.) Seem Gardenia resinifera. Roth.
0.50
0.06
0.48
0
Rhus mysorensis (Don)
0.48
0.01
Pithecellobium dulce (Roxb.) Benth.
0.45
0
Butea monosperma (Lam.) Kuntze
0.32
0.02
Pavetta indica L.
0.31
0
Aglaia elaeagnoidea (A. Juss.) Benth.
0.28
0.01
Commiphora caudata Engl.
0.28
0
Manilkara hexandra (Roxb.) Dubard
0.23
0.01
Pisonia sechellarum F.Friedmann
0.22
0.00
Cassia fistula L.
0.16
0.01
Terminalia bellirica Roxb.
0.14
0.00
Ficus glomerata Roxb.
0.07
0.01
Name of the species
TC (Mg/ha)
Pterolobium hexapetalum (Roth) Santapau and Wagh Combretum albidum G.Don
52.36
Acacia caesia (L.) Willd
16.42
44.26
Ziziphus oenoplia (L.) Miller
5.86
Toddalia asiatica (L.) Lam.
2.54
Leptadenia reticulata (Retz.) W. and A.
1.96
Plecospermum spinosum Trecur.
1.64
Reissantia indica (Willd.) N. Hallé
1.59
Wattakaka volubilis (L. f.) Stapf
1.21
Diplocyclos palmatus (L.) C. Jeffrey
1.17
tree species richness (r = 0.323) while herbaceous community density and basal area exhibited a negative correlation with AGB. Similarly, juvenile basal area showed a significant negative relationship with total woody carbon stock (r = −0.665). However, the total woody carbon stock showed a significant positive relationship with vegetation parameters, viz., tree basal area (R2 = 0.977; P = 0.0000), total plant species richness (R 2 = 0.283; P = 0.0025), tree species richness (r = 0.324), climber species richness (r = 0.419) and density (r = 0.488), and herb species richness (r = 0.39). Similarly, large girth trees showed a significant positive relationship with aboveground biomass of adult trees (≥100 cm GBH; R2 = 0.5422, P = 0.000; ≥200 cm GBH, R2 = 0.6253, P = 0.000) and total woody carbon stock (≥100 cm GBH, R2 = 0.5422, P = 0.000; ≥200 cm GBH, R2 = 0.6116, P = 0.000; Fig. 5).
Cassia didymobotrya Fresen.
0.06
0
Cordia monoica Roxb.
0.05
0
Annona squamosa L.
0
0.001
Drypetes deplanchei (Brongn. and Gris) Merr. Ficus hispida L. f.
0
0.01
Discussion
0
0.01
Murraya koenigii (L.) Spreng.
0
0.00
Parkinsonia aculeata L.
0
0.01
Tropical forests accumulate significant amounts of C in biomass. The total biomass estimated in woody vegetation in the present study was 8851.05 Mg/30 ha, and it ranged from 85.02 to 723.46 Mg/ha, with a mean of 295.04 Mg/ha. The present study values are comparable to the world tropical forest average biomass values (Brown et al. 1993; Slik et al. 2010; Navar-Chaidez 2011; Becknell et al. 2012; Lewis et al. 2013; Berenguer et al. 2014). However, there is a significant variation in biomass among various forest types and geographic locations which could be attributed to differences in
variables were presented in Table 6 and Figs. 3, 4 and 5. A significant positive relationship was observed between AGB and vegetation parameters, i.e., the tree basal area (R2 = 0.978; P = 0.0000), total plant species richness (R2 = 0.2638; P = 0.0038) and climber density (r = 0.490), climber species richness (r = 0.396) and
187
Environ Monit Assess (2017) 189:187
Page 8 of 18
Table 5 Family-wise contribution of woody species carbon stock in the tropical dry deciduous forest of Sathanur reserve forest, Eastern Ghats S.No.
Name of the family
ATC
JTC
LC
TWC
%C
1
Mimosaceae
2393.91
17.54
0
2411.45
61.19
2
Fabaceae
345.16
1.16
16.54
362.86
9.21
3
Flindersiaceae
256.19
6.85
0
263.04
6.68
4
Caesalpiniaceae
80.13
0.2
52.36
132.69
3.37
5
Myrtaceae
120.6
0.02
0
120.62
3.06
6
Meliaceae
110.15
0.91
0
111.06
2.82
7
Ebenaceae
77.03
1
0
78.03
1.98
8
Moringaceae
60.51
0.01
0
60.52
1.54
9
Hernandiaceae
55.54
0.1
0
55.64
1.41
10
Combretaceae
5.81
0
44.26
50.07
1.27
11
Rhamnaceae
33.45
0.18
5.86
39.49
1.00
12
Rubiaceae
37.31
0.78
0
38.09
0.97
13
Euphorbiaceae
32.42
0.67
0.25
33.34
0.85
14
Rutaceae
27.06
0.69
2.54
30.29
0.77
15
Sapindaceae
23.93
0.07
0.19
24.19
0.61
16
Anacardiaceae
23.98
0.03
0
24.01
0.61
17
Apocynaceae
21.63
0.34
0.02
21.99
0.56
18
Moraceae
16.49
0.06
1.64
18.19
0.46
19
Verbenaceae
9.39
0.06
1.09
10.54
0.27
20
Alangiaceae
9.86
0.27
0
10.13
0.26
21
Arecaceae
8.62
0.05
0
8.67
0.22
22
Tiliaceae
7.41
0.01
0
7.42
0.19
23
Erythroxylaceae
4.34
0.05
0
4.39
0.11
24
Strychnaceae
4.3
0.05
0
4.35
0.11
25
Ulmaceae
3.62
0
0
3.62
0.09
26
Asclepiadaceae
0
0
3.52
3.52
0.09
27
Capparaceae
2.05
0.03
0.23
2.31
0.06
28
Cucurbitaceae
0
0
1.65
1.65
0.04
29
Celastraceae
0
0
1.59
1.59
0.04
30
Lamiaceae
1.33
0.01
0
1.34
0.03
31
Simaroubaceae
0.86
0.06
0
0.92
0.02
32
Sterculiaceae
0.76
0
0
0.76
0.02
33
Menispermaceae
0
0
0.64
0.64
0.02
34
Bignoniaceae
0.5
0.06
0
0.56
0.01
35
Vitaceae
0
0
0.51
0.51
0.01
36
Opiliaceae
0
0
0.42
0.42
0.01
37
Nyctaginaceae
0.22
0
0.14
0.36
0.01
38
Loganiaceae
0
0
0.35
0.35
0.01
39
Burseraceae
0.28
0
0
0.28
0.01
40
Sapotaceae
0.23
0.01
0
0.24
0.01
41
Liliaceae
0
0
0.22
0.22
0.01
42
Linaceae
0
0
0.19
0.19
0.00
43
Oleaceae
0
0
0.06
0.06
0.00
44
Boraginaceae
0.05
0
0
0.05
0.00
Environ Monit Assess (2017) 189:187
Page 9 of 18 187
Table 5 (continued) S.No.
Name of the family
45
Annonaceae
46
Dioscoreaceae
ATC
JTC
LC
TWC
%C
0.001
0.001
0
0.002
0.00
0
0
0.001
0.001
0.00
ATC adult tree carbon, JTC juvenile tree carbon, LC liana carbon, TWC total woody carbon
Lewis et al. (2013), Berenguer et al. (2014), Becknell and Powers (2014), and Hu et al. (2015). In the present study, woody biomass showed significant spatial variations among the plots which may be due to the difference in the level of anthropogenic pressure among the plots, i.e., selective removal of trees in different periods. Nevertheless, few plots in the present study contain higher total woody biomass which may be due to the presence of many trees of large-diameter classes, which coincides with the report of Pande (2005) who observed
vegetation structure, species composition, edaphic factors, rainfall pattern, dry season length, slope, aspect, etc. Biomass is also determined by tree density, height, and basal area at any given location. These parameters contribute to the aboveground biomass which differs with plots, habitat, forest succession stage, composition of forest, species variability, nature of terrain, edaphic factors, level and type of anthropogenic disturbances, etc. as stated by Brunig (1983), Whitmore (1984), Navar et al. (2002), Joshi and Ghose (2014), Slik et al. (2010),
Table 6 Correlation (r value) between predictor variables with tree aboveground biomass (AGB), and total woody carbon stock (TWC) in the tropical dry deciduous forest of Sathanur reserve forest, Eastern Ghats Predictor variables
AGB r value
TWC F value
Level of significance
r value
F value
Level of significance
Total plant species richness (all life forms)
0.490
9.994
0.004
0.508
9.745
0.004
Tree species richness
0.323
3.269
NS
0.324
3.285
NS
Tree density
0.146
0.612
NS
0.151
0.650
NS
Tree basal area
0.989
1247.94
0.000
0.988
1182.122
0.000
Shannon index (tree)
0.102
0.948
NS
–
–
–
Dominance index (tree)
−0.065
0.117
NS
–
–
–
Mean diameter of trees
0.239
1.690
NS
0.243
1.757
NS
Adult tree basal area
–
–
–
0.994
0.485
NS
Juvenile basal area
–
–
–
−0.665
0.556
NS
Shrub species richness
0.046
0.589
NS
0.061
0.105
NS
Shrub Density
0.214
1.346
NS
0.194
1.10
NS
Shrub basal area
0.187
1.016
NS
0.188
1.022
NS
Herb species richness
0.378
4.672
0.039
0.390
5.009
0.033
Herb density
−0.218
1.405
NS
−0.220
1.427
NS
Herb basal area
−0.211
1.308
NS
−0.215
1.355
NS
Climber species richness
0.396
5.213
0.030
0.419
5.988
0.021
Climber density
0.490
8.882
0.006
0.488
0.874
0.006
Climber basal area
0.247
1.831
NS
0.290
2.566
NS
Soil pH
0.058
0.096
NS
0.045
0.058
NS
Soil moisture
0.230
1.569
NS
0.228
1.533
NS
SOC
0.110
0.39
NS
0.122
0.485
NS
Bulk density
–
–
–
0.140
0.555
NS
NS non-significant
Environ Monit Assess (2017) 189:187
Page 10 of 18
Fig. 3 Relationship between aboveground biomass and predictor variables in the tropical dry deciduous forest of Sathanur reserve forest, Eastern Ghats
y = -0.0219x2 + 7.5782x - 133.38 R² = 0.2638 (P = 0.0038)
700 Aboveground biomass (Mg/ha)
187
600 500 400 300 200 100 0 30
Aboveground biomass (Mg/ha)
700
40
50 60 70 Total plant species richness
80
90
y = 15.782x - 49.373 R² = 0.9781 (P=0.000)
600 500 400 300 200 100 0 0
10
Aboveground biomass (Mg/ha)
700
20 30 Tree basal area (m2/ha)
40
50
y = -0.006x2 + 2.5923x + 109.7 R² = 0.2728 (P=0.0059)
600 500 400 300 200 100 0 0
that the higher basal area plots accumulate greater biomass compared to other plots having a juvenile population of trees. Our results clearly suggested that few plots, viz., 7, 13, 15, and 21, have moderate density of trees (336, 512, 435, and 449 respectively) but have more biomass (412, 723,523, and 434 Mg/ha) and carbon stock (183, 322, 233, and 193 Mg/ha) due to the occurrence of larger diameter size class trees like Pongamia pinnata, Syzygium cumini, Azadirachta indica, Albizia amara, Chloroxylon swietenia, Ficus benghalensis, and Albizia lebbeck. These tree species are the major contributors of biomass and carbon stock among all tree species in these plots. Large-diameter trees are lesser in number, which harbor higher biomass. Large-diameter class trees contribute to more aboveground biomass in forests that was confirmed with the findings of earlier workers (Brown and Lugo 1992; Brown et al. 1995; Brown 1996; Clark and Clark 2000) who stated that about 50% of aboveground biomass was contributed by large trees (>70 cm DBH). Lewis et al. (2013) also
50
100 150 200 Climber density(No./0.125 ha)
250
300
found that the stem density in African tropical forests is lesser than those in Borneo (Slik et al. 2010) and in Amazonia (Lewis et al. 2004). At the same time, they recorded highest average biomass in African forest than in Borneo (Slik et al. 2010) and in Amazonia (Malhi et al. 2006). The present study values envisage that tree density did not show any significant relationship with biomass. Similarly, Chiba (1998) observed that the aboveground biomass is strongly associated with tree diameter but not the density. These plots are located near the stream or places that receive the seepage water from agriculture fields which also favor growth. In addition, plots 13 and 15 are located in the middle in between stream and roads and far away from human settlement, so the level of anthropogenic pressure is minimum or very low which resulted in retention of more biomass and carbon stock. The mean aboveground biomass of trees (≥10 cm DBH) in tropical dry deciduous forest of Sathanur reserve forest was 245.9 ± 24.2 Mg/ha, which is close to the
Fig. 4 Relationship between total woody carbon stock and predictor variables in the tropical dry deciduous forest of Sathanur reserve forest, Eastern Ghats
Page 11 of 18 187 Total woody carbon (Mg/ha)
Environ Monit Assess (2017) 189:187 y = -0.0062x2 + 3.3699x - 51.247 R² = 0.2828 (P = 0.0025)
400 300 200 100 0
Total woody carbon (Mg/ha)
30 350 300 250 200 150 100 50 0
Total woody carbon (Mg/ha)
50 60 70 Total plant species richness
80
90
y = 8.0632x - 19.47 R² = 0.9769 (P=0.000)
0
10
20 30 Tree basal area (m2/ha)
40
50
y = 32.806x2 - 160.97x + 279.11 R² = 0.4804 (P = 0.000)
350 300 250 200 150 100 50 0 0
Total woody carbon (Mg/ha)
40
0.5
1 1.5 2 Juvenile basal area (m2/ha)
2.5
3
y = -0.0033x2 + 1.3854x + 59.638 R² = 0.2757 (P = 0.006)
350 300 250 200 150 100 50 0 0
Amazonian average (288.6 Mg/ha; Malhi et al. 2006) while the value was 46.2% lower than the Bornean average (457.1 Mg/ha; Slik et al. 2010) and 37.9% lower than the African average (395.7 Mg/ha; Lewis et al. 2013). However, the present study values of aboveground biomass of tree community were comparable with several studies in tropical forests of Eastern Ghats and elsewhere (Murphy and Lugo 1986; Singh and Singh 1991; Clark and Clark 2000; Chave et al. 2003; Mohanraj et al. 2011; Slik et al. 2010; Usuga et al. 2010; Juwarkar et al. 2011; Bhat and Ravindranath 2011; Chaturvedi et al. 2011a, b, c; Becknell et al. 2012; Pan et al. 2013; Sundarapandian et al. 2013; Lewis et al. 2013; Pawar et al. 2014; Salunkhe
50
100 150 200 Climber density (No./0.125 ha)
250
300
et al. 2014, 2016; Becknell and Powers 2014; Ekoungoulou et al. 2014a, b; Pragasan 2015a, b; Hu et al. 2015; Lee et al. 2015; Borah et al. 2015; PradoJunior et al. 2016; Paladines and Ruiz 2016; Spracklen and Righelato 2016). Some plots which had a high density of juveniles (≥3.2–<10 cm) usually contribute to less biomass. Analyses have shown that forest plots (25, 1, 12, 24, and 9) hoarded low biomass (85, 104, 118, 149, and 153 Mg/ha respectively) and carbon stocks (37.9, 46.4, 52.8, 66.2, and 68.1 Mg/ha respectively). In addition to that, these plots contain moderate to high tree density (629, 821, 500, 456, and 438 individuals/ha respectively). The lowest biomass and carbon obtained in these
Environ Monit Assess (2017) 189:187
Page 12 of 18
Fig. 5 Relationship between numbers of large girth trees with aboveground biomass (AGB), total woody biomass, and total woody carbon stock in the tropical dry deciduous forest of Sathanur reserve forest, Eastern Ghats
Total woody carbon (Mg/ha)
187
350 300 250 200 150 100 50 0
y = 0.0003x2 + 1.3382x + 72.103 R² = 0.5422 (P = 0.000)
AGB of adult trees (Mg/ha)
0
Total woody carbon (Mg/ha)
40 60 80 100 Number of individuals (≥100cm GBH)
120
140
800 600 400
y = -0.2045x2 + 20.641x + 160.6 R² = 0.6253 (P = 0.000)
200 0 0 400
5
10 15 20 25 Number of individuals (≥ 200 cm GBH)
30
35
y = 0.0003x2 + 1.3382x + 72.103 R² = 0.5422 (P = 0.000)
300 200 100 0 0
Total woody carbon (Mg/ha)
20
350 300 250 200 150 100 50 0
20
40 60 80 100 Number of individuals (≥100cm GBH)
120
140
y = -0.1116x2 + 10.637x + 87.853 R² = 0.6116 (P = 0.000) 0
plots could be attributed to either the removal of large trees by human interventions in the past or are secondary forests which do not yet have large trees. Berenguer et al. (2014) also observed that the human disturbances at forest edge from logging showed a negative impact on biomass and carbon stock. Large-diameter class trees contributed to a greater proportion of aboveground biomass that indicates the significance of large trees in C storage, but one cannot underestimate the critical role of small-sized trees (<30 cm DBH) which would stock more C in the future owing to their high sequestration potential. Furthermore, it has been observed by Brown (1997) that small-sized trees share maximum aboveground biomass in those forests, where aboveground biomass is <300 Mg/ha.
10 20 30 Number of individuals (≥200cm GBH)
40
Carbon storage rate variations in tropical dry forests are due to variations in the availability of soil moisture and nutrients (Chaturvedi et al. 2011a, b, c). The mean value (109.5 ± 10.8 Mg C/ha) of aboveground carbon stock obtained in the tropical dry deciduous forest of Sathanur reserve forest lies within the global range (14– 123 Mg C/ha) of aboveground carbon stock for the tropical dry forest (Murphy and Lugo 1986) and the range of Mexican tropical forests by Navar-Chaidez (2011). The total woody carbon in the 30-ha plots in tropical dry deciduous forest ranged from 37.86 to 322.16 Mg/ ha with a mean value of 131.38 Mg/ha which is close to the global range of tropical deciduous forests reported earlier (39–334 Mg/ha, Becknell et al. 2012; 1.6– 409 Mg/ha; Becknell and Powers 2014). The present
Environ Monit Assess (2017) 189:187
study values are comparable to other parts of Eastern Ghats as well and in the tropical world (Singh and Singh 1991; Malhi et al. 1999; Mohanraj et al. 2011; Slik et al. 2010; Chaturvedi et al. 2011a, b, c; Becknell et al. 2012; Chaturvedi and Raghubanshi 2013; Pan et al. 2013; Sundarapandian et al. 2013; Lewis et al. 2013; Pawar et al. 2014; Pragasan 2015a, b; Salunkhe et al. 2014; Becknell and Powers 2014; Ekoungoulou et al. 2014a, b; Hu et al. 2015; Lee et al. 2015; Prado-Junior et al. 2016; Paladines and Ruiz 2016; Spracklen and Righelato 2016). The spatial variation of carbon stocks among the plots in the present study may be due to soil characteristics, level of anthropogenic pressure, associated microclimatic conditions, and status of stand structure. In the present study, Albizia amara accumulated the greatest biomass and carbon stocks (58.31%) except in two plots (24 and 25), where Chloroxylon swietenia was the highest. Similarly, Albizia amara stored maximum biomass and carbon stock in the Kalrayan hills and Bodamalai hills, Eastern Ghats (Pragasan 2015a, b). In general, Albizia amara was the dominant species in terms of abundance and basal area which reflected in biomass and carbon stocks. Similarly, single-species dominance on density, basal area, and carbon stock was also observed by Sahu et al. (2016) in the Eastern Ghats. Among 46 families recorded in the study plots, Mimosaceae contributed to greater biomass and carbon stock with 2411.5 Mg carbon (61.19%) followed by Fabaceae (9.21%; 362.86 Mg), Flindersiaceae (6.68%; 263.04 Mg), Caesalpiniaceae (3.37%; 132.69 Mg), and Myrtaceae (3.06%; 120.62 Mg). The present study revealed that Albizia amara and Chloroxylon swietenia are the important tree species to withstand and sequester more carbon in dry tropical forest ecosystem even under anthropogenic pressure. This study also indicates the need for prioritization of Albizia amara and Chloroxylon swietenia species conservation, in order to achieve significant carbon stocks in tropical dry deciduous forest ecosystem of Eastern Ghats. Aboveground biomass and carbon stocks of forests are also influenced by density, height, mean stem diameter, and wood specific gravity of trees (Poorter et al. 2015).A significant positive relationship was found between carbon stock and stand (plot) basal area, i.e., the tree basal area vs. total woody carbon stock (R2 = 0.977), while juvenile basal area showed a significant negative relationship with woody carbon stock (R2 = 0.4804). A strong positive relationship was observed between aboveground biomass and basal area,
Page 13 of 18 187
which was in agreement with several earlier findings (Murali et al. 2005; Cannell 1984; Rai and Proctor 1986; Lewis et al. 2013; Dar and Sundarapandian 2015; Sundarapandian et al. 2014a, b; Poorter et al. 2015; Sahu et al. 2016). A direct relationship exists among basal area, biomass, and carbon stock because allometric equations for estimation have been developed based on prime factor, either diameter or basal area of trees. It is obvious that diameter and biomass showed high significant relationship, whereas juvenile tree diameter showed negative relationship with total woody biomass and carbon which could be attributed to level of anthropogenic pressure. Since disturbance regime reduces the adult population and create favorable microclimate for juvenile population, this could be the reason for negative relationship that existed between juvenile basal area and total woody carbon stock. However, no significant relationship was observed for carbon stock with an abundance of woody species in the present study. In contrast, large-diameter tree abundance showed strong positive relationship with biomass (stems ≥100 cm GBH, R2 = 0.542; stems ≥200 cm GBH, R2 = 0.6253) and total woody carbon stock (stems ≥100 cm GBH, R2 = 0.542; stems ≥200 cm GBH, R2 = 0.6116). This explains that the density of large-diameter trees, stand basal area, and average stand diameter would be the best predictors of woody carbon stock in the tropical deciduous forest than overall tree density as suggested by Poorter et al. (2015). Similarly, Slik et al. (2013) also observed that 70% of aboveground biomass was determined by the number of large trees (>70 cm DBH) while analyzing 120 lowland tropical forest in pantropical forests. This is also in agreement with another analysis in the neotropical forest, which contains 30% less biomass than palaeotropical forests because of the least abundance or lack of large-diameter class trees (Poorter et al. 2015). Larger trees play a vital role in system functioning, not only in storing more carbon but also because they form a canopy which minimizes the establishment of ruderal and invasive species. This analysis enlightens that the structural attributes of forests determine the woody biomass and carbon stock of tropical dry deciduous forests. In addition to that rainfall, availability of water, length of dry season, species traits, soil fertility, and local disturbance level may determine the retention of biomass and carbon stock (Laurance et al. 1999; Navar-Chaidez 2011; Quesada et al. 2012; Slik et al. 2013; Lewis et al. 2013; Fauset et al. 2015; Poorter et al. 2015).
187
Page 14 of 18
Conclusion The total biomass of woody vegetation and carbon stock showed spatial variation among the 30-ha plots. Adult tree species contributed 94.8% of total woody carbon stock, followed by lianas (3.4%) and juvenile tree population (1.2%). The spatial variation of carbon stocks among the plots in the present study may be due to soil characteristics, level of anthropogenic pressure, associated microclimatic conditions, and status of stand structure. In addition, larger diameter trees play a vital role in spatial variation in biomass and carbon stocks here, which enlighten that the structural attributes of forests determine the woody biomass and carbon stock of tropical dry deciduous forest. The data (diversity and carbon stock) obtained in the present study would serve as a baseline data for tropical dry deciduous forest and would be useful to assess both national and global level carbon stocks. Acknowledgements The first author thanks the University Grants Commission (UGC), Government of India for fellowship. We are grateful to forest officials, Department of Forest, Tamil Nadu, for permission to conduct the field work. We thank the two anonymous reviewers for their valuable comments and suggestions to enhance quality of the article.
References Becknell, J. M., & Powers, J. S. (2014). Stand age and soils as drivers of plant functional traits and aboveground biomass in secondary tropical dry forest. Canadian Journal of Forest Research, 44(6), 604–613. Becknell, J. M., Kucek, L. K., & Powers, J. S. (2012). Aboveground biomass in mature and secondary seasonally dry tropical forests: a literature review and global synthesis. Forest Ecology and Management, 276, 88–95. Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rodenbeck, C., Arain, A. M., Baldocchi, D., Bonan, G. B., Bondeau, A., Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis, H., Oleson, K. W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C., Woodward, F. I., & Papale, D. (2010). Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science, 329, 834–838. Berenguer, E., Ferreira, J., Gardner, T. A., Aragao, L. E. O. C., De Camargo, P. B., Cerri, C. E., Durigan, M., Oliveira, R. C. D., Vieira, I. C. G., & Barlow, J. (2014). A large-scale field assessment of carbon stocks in human-modified tropical forests. Global Change Biology, 20(12), 3713–3726. Bhat, D. M., & Ravindranath, N. H. (2011). Above-ground standing biomass and carbon stock dynamics under a varied degree of anthropogenic pressure in tropical rain
Environ Monit Assess (2017) 189:187 forest of Uttar Kannada District, Western Ghats, India. Taiwania, 56(2), 85–96. Bijalwan, A., Swamy, S. L., Sharma, C. M., Sharma, N. K., & Tiwari, A. K. (2010). Land-use, biomass and carbon estimation in dry tropical forest of Chhattisgarh region in India using remote sensing and GIS. Journal of Forest Research, 21(2), 161–170. Borah, M., Das, D., Kalita, J., Boruah, H. P. D., Phukan, B., & Neog, B. (2015). Tree species composition, biomass and carbon stocks in two tropical forest of Assam. Biomass and Bioenergy, 78, 25–35. Brienen, R. J. W., Brienen, R. J. W., Phillips, O. L., Feldpausch, T. R., Gloor, E., Baker, T. R., Lloyd, J., Lopez-Gonzalez, G., Monteagudo-Mendoza, A., Malhi, Y., Lewis, S. L., Vásquez Martinez, R., Alexiades, M., Álvarez Dávila, E. A., AlvarezLoayza, P., Andrade, A., Aragao, L. E. O. C., AraujoMurakami, A., Arets, E. J. M. M., Arroyo, L., Aymard, C. G. A., Banki, O. S., Baraloto, C., Barroso, J., Bonal, D., Boot, R. G. A., Camargo, J. L. C., Castilho, C. V., Chama, V., Chao, K. J., Chave, J., Comiskey, J. A., Cornejo Valverde, F., Costa, L. D., Oliveira, E. A. D., Di Fiore, A., Erwin, T. L., Fauset, S., Forsthofer, M., Galbraith, D. R., Grahame, E. S., Groot, N., Hérault, B., Higuchi, N., Honorio Coronado, E. N., Keeling, H., Killeen, T. J., Laurance, W. F., Laurance, S., Licona, J., Magnussen, W. E., Marimon, B. S., MarimonJunior, B. H., Mendoza, C., Neill, D. A., Nogueira, E. M., Nunez, P., Pallqui Camacho, N. C., Parada, A., PardoMolina, G., Peacock, J., Pena-Claros, M., Pickavance, G. C., Pitman, N. C. A., Poorter, L., Prieto, A., Quesada, C. A., Ramırez, F., Ramirez-Angulo, H., Restrepo, Z., Roopsind, A., Rudas, A., Salomao, R. P., Schwarz, M., Silva, N., Silva-Espejo, J. E., Silveira, M., Stropp, J., Talbot, J., Ter Steege, H., Teran-Aguilar, J., Terborgh, J., Thomas-Caesar, R., Toledo, M., Torello-Raventos, M., Umetsu, R. K., Van der Heijden, G. M. F., Van der Hout, P., Guimaraes Vieira, I. C., Vieira, S. A., Vilanova, E., Vos, V. A., & Zagt, R. J. (2015). Long-term decline of the Amazon carbon sink. Nature, 519, 344–348. Brown, S. (1996). Tropical forests and the global carbon cycle: estimating state and change in biomass density. Forest ecosystems, forest management and the global carbon cycle. Springer Berlin Heidelberg. Brown, S. (1997). Estimating biomass and biomass change in tropical forests. A primer. FAO Forestry Paper 134. Rome: Food and Agriculture Organization of the United Nations. Brown, S., & Lugo, A. E. (1992). Aboveground biomass estimates for tropical moist forests of the Brazilian Amazon. Interciencia Caracas, 17(1), 8–18. Brown, S., Iverson, L. R., Prasad, A., & Liu, D. (1993). Geographic distribution of carbon in biomass and soils of tropical Asian forests. Geocarto International, 8, 45–59. Brown, I. F., Martinelli, L. A., Thomas, W. W., Moreira, M. Z., Ferreira, C. A. C., & Victoria, R. A. (1995). Uncertainty in the biomass of Amazonian forests: an example from Rondonia, Brazil. Forest Ecology and Management, 75, 175–189. Brunig, E. F. (1983). Structure and growth. In F. B. Golley (Ed.), Ecosystems of the world 14 a, tropical rain Forest ecosystems: structure and function (pp. 49–75). New York: Elsevier Scientific Publication.
Environ Monit Assess (2017) 189:187 Cairns, M. A., Brown, S., Helmer, E. H., & Baumgardner, G. A. (1997). Root biomass allocation in the world’s upland forests. Oecologia, 111, 1–11. Cannell, M. G. R. (1984). Woody biomass of forest stands. Forest Ecological Management, 8, 299–312. Cao, T., Valsta, L., & Makela, A. (2010). A comparison of carbon assessment methods for optimizing timber production and carbon sequestration in scots pine stands. Forest Ecology and Management, 260, 1726–1734. Champion, H. G., & Seth, S. K. (1968). A revised survey of the forest types of India. New Delhi: Govt. of India Press Pages, 404. Chaturvedi, R. K., & Raghubanshi, A. S. (2013). Aboveground biomass estimation of small diameter woody species of tropical dry forest. New Forests, 44, 509–519. Chaturvedi, R. K., & Raghubanshi, A. S. (2015). Assessment of carbon density and accumulation in mono- and multi-specific stands in teak and sal forests of a tropical dry region in India. Forest Ecology and Management, 339, 11–21. Chaturvedi, R. K., Raghubanshi, A. S., & Singh, J. S. (2011a). Carbon density and accumulation in woody species of tropical dry forest in India. Forest Ecology and Management, 262, 1576–1588. Chaturvedi, R. K., Raghubanshi, A. S., & Singh, J. S. (2011b). Effect of small-scale variations in environmental factors on the distribution of woody species in tropical deciduous forests of Vindhyan highlands, India. Journal of Botany Article ID 297097, 10 pp. doi:10.1155/2011/297097. Chaturvedi, R. K., Raghubanshi, A. S., & Singh, J. S. (2011c). Plant functional traits with particular reference to tropical deciduous forests: a review. Journal of Biosciences, 36, 963–981. Chaturvedi, R. K., Raghubanshi, A. S., & Singh, J. S. (2012a). Effect of grazing and harvesting on diversity, recruitment and carbon accumulation of juvenile trees in tropical dry forests. Forest Ecology and Management, 284, 152–162. Chaturvedi, R. K., Raghubanshi, A. S., & Singh, J. S. (2012b). Biomass estimation of dry tropical woody species at juvenile stage. Scientific World Journal. doi:10.1100/2012/790219. Chave, J., Condit, R., Lao, S., Caspersen, J. P., Foster, R. B., & Hubbell, S. P. (2003). Spatial and temporal variation of biomass in a tropical forest: results from a large census plot in Panama. Journal of Ecology, 91, 240–252. Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eames, D., Folster, H., Formard, F., Higuchi, N., Kira, T., Lescure, J. P., Nelson, B. W., Ogawa, H., Puig, H., Riera, B., & Yamakura, T. (2005). Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145, 87–99. Chhabra, A., Palria, S., & Dadhwal, V. K. (2002). Spatial distribution of phytomass carbon in Indian forests. Global Change Biology, 8, 1230–1239. Chiba, Y. (1998). Architectural analysis of relationship between biomass and basal area based on pipe model theory. Ecological Modelling, 108, 219–225. Clark, D. B., & Clark, D. A. (2000). Landscape-scale variation in forest structure and biomass in a tropical rain forest. Forest Ecology and Management, 137(3), 185–198. Cordero, L. D. P., & Kanninen, M. (2002). Wood specific gravity and aboveground biomass of bombacopsis quinata
Page 15 of 18 187 p la n t at i o ns in C o s t a R i c a. F ores t E c o l o g y a n d Management, 165, 3–9. Ekoungoulou, R., Liu, X., Loumeto, J. J., Averti Ifo, S., Bocko, Y. E., Koula, F. E., & Shukui, N. (2014a). Tree Allometry in tropical forest of Congo for carbon stocks estimation in above-ground biomass. Open Journal of Forestry, 4, 481–491. Ekoungoulou, R., Liu, X., Ifo, S. A., Loumeto, J. J., & Folega, F. (2014b). Carbon stock estimation in secondary forest and gallery forest of Congo using allometric equations. International Journal of Scientific & Technology Research, 3, 465–474. Fauset, S., Johnson, M. O., Gloor, M., Baker, T. R., Monteagudo, A., Brienen, R. J., Feldpausch, T. R., Lopez-Gonzalez, G., Malhi, Y., Ter Steege, H., Pitman, N. C., Vieira, S. A., Steininger, M., Rodrıguez, C. R., Restrepo, Z., Muelbert, A. E., Lewis, S. L., Pickavance, G. C., & Phillips, O. L. (2015). Hyperdominance in Amazonian forest carbon cycling. Nature Communications, 6, 6857. Foley, J. A. (1995). An equilibrium model of the terrestrial carbon budget. Tellus, 47B, 310–319. Foley, J. A., Costa, M. H., Delire, C., Ramankutty, N., & Snyder, P. (2003). Green surprise? How terrestrial ecosystems could affect earth’s climate. Frontiers in Ecology and the Environment, 1, 38–44. FSI. (2015). State of the forest report 2015. Dehradun: Forest Survey of India, Ministry of Environment and Forests. Gisel, R., Brown, S., Chapmen, J., Lugo, A.E. (1992). Wood densities of tropical tree species. General technical Report; S0–88 New Orleans, Louisiana. Haripriya, G. S. (2000). Estimates of biomass in Indian forests. Biomass and Bioenergy, 19(4), 245–258. Houghton, R. A., Hall, F., & Goetz, S. J. (2009). Importance of biomass in the global carbon cycle. Journal of Geophysical Research, 114, G00E03. Hu, Y., Su, Z., Li, W., Li, J., & Ke, X. (2015). Influence of tree species composition and community structure on carbon density in a subtropical forest. PloS One, 10(8), 136984. IPCC. (2003). Good practice guidance for land use, land-use change and forestry. Hayama: Institute for Global Environmental Strategies (IGES). IPCC. (2007). Intergovernmental Panel on Climate Change (IPCC). Climate Change 2007: the physical science basis: working group I contribution to the fourth assessment report of the IPCC. Cambridge: Cambridge University Press. IPCC. (2014). Contribution of working group III to the fifth assessment report of the intergovernmental panel on Climate Change (eds Edenhofer O, Pichs-Madruga R, Sokona Y et al.) (pp. 1–30). Cambridge: Cambridge University Press. Jackson, R. B., Mooney, H. A., & Schulze, E. D. (1997). A global budget for fine root biomass, surface area, and nutrient contents. Proceedings of Natural Academic Science, USA, 94, 7362–7366. Jayakumar, S., Arockiasamy, D. I., & Britto, S. (2002). Forest type mapping and vegetation analysis in part of Kolli hills, Eastern Ghats of Tamil Nadu. Tropical Ecology, 43(2), 345–349. Jobbágy, E. G., & Jackson, R. B. (2000). The vertical distribution of soil carbon and its relation to climate and vegetation. Ecological Applications, 10, 423–436.
187
Page 16 of 18
Joos, F., & Spahni, R. E. (2008). Rates of change in natural and anthropogenic radiative forcing over the past 20,000 years. Proceedings of the National Academy of Sciences (PNAS), 105(5), 1425–1430. Joshi, H. G., & Ghose, M. (2014). Community structure, species diversity, and aboveground biomass of the Sundarbans mangrove swamps. Tropical Ecology, 55, 283–303. Joshi, N.R., Karki, S., Adhikari, M.D., Udas, E., Sherpa, S., Karky, B.S., Chettri, N., Kotru, R. & Ning, W. 2015. Development of allometric equations for Paulownia tomentosa (Thunb.) to estimate biomass and carbon stocks: an assessment from the ICIMOD Knowledge Park, Godavari, Nepal. ICIMOD Working Paper 2015/10, ICIMOD. International Centre for Integrated Mountain Development, GPO Box 3226, Kathmandu, Nepal. 41p. Junior, L. R. P., Andrade, E. M., Palácio, H. A. Q., Raymer, P. C. L., Filho, J. C. R., & Pereira, F. J. S. (2016). Carbon stocks in a tropical dry forest in Brazil. Review of Ciência Agronomica, 47(1), 32–40. Juwarkar, A. A., Varghese, A. O., Singh, S. K., Aher, V. V., & Thawale, P. R. (2011). Carbon sequestration potential in aboveground biomass of natural reserve forest of central India. International journal of agriculture: Research and Review, 1(2), 80–86. Kerr, R. A. (2007). Global warming is changing the world. Science, 316, 188–189. Kohl, M., Lasco, R., Cifuentes, M., Jonsson, O., Korhonen, K. T., Mundhenk, P., Navar, J. J., & Stinson, G. (2015). Changes in forest production, biomass and carbon: results from the 2015 UN FAO Global Forest Resource Assessment. Forest Ecology and Management, 352, 21–34. Kyoto Protocol, (1997). Kyoto Protocol of the United Nations Framework Convention on Climate Change, FCCC/CP/ 1997/7/Add.1, Decision 1CP.3, Annex 7 UN. Laurance, W. F., Fearnside, P. M., Laurance, S. G., Delamonica, P., Lovejoy, T. E., Rankin-de Merona, J. M., Chambers, J. Q., & Gascon, C. (1999). Relationship between soils and Amazon forest biomass: a landscape-scale study. Forest Ecology and Management, 118, 127–138. Lee, S., Lee, D., Yoon, T. K., Salim, K. A., Han, S., Yun, H. M., Yoon, M., Kim, E., Lee, W. K., Davies, S. J., & Son, Y. (2015). Carbon stocks and its variations with topography in an intact lowland mixed dipterocarp forest in Brunei. Journal of Ecology and Environment, 38(1), 75–84. Lewis, S. L., Phillips, O. L., Sheil, D., Vinceti, B., Baker, T. R., Brown, S., Graham, A. W., Higuchi, N., Hilbert, D. W., Laurance, W. F., & Lejoly, J. (2004). Tropical forest tree mortality, recruitment and turnover rates: calculation, interpretation and comparison when census intervals vary. Journal of Ecology, 92, 929–944. Lewis, S. L., Sonke, B., Sunderland, T., Begne, S. K., LopezGonzalez, G., van der Heijden, G. M. F., Phillips, O. L., Affum-Baffoe, K., Baker, T. R., Banin, L., Bastin, J.-F., Beeckman, H., Boeckx, P., Bogaert, J., De Canniere, C., Chezeaux, E., Clark, C. J., Collins, M., Djagbletey, G., Djuikouo, M. N. K., Drois-sart, V., Doucet, J.-L., Ewango, C. E. N., Fauset, S., Feldpausch, T. R., Foli, E. G., Gillet, J.F., Hamilton, A. C., Harris, D. J., Hart, T. B., de Haulleville, T., Hladik, A., Hufkens, K., Huy-gens, D., Jeanmart, P., Jeffery, K. J., Kearsley, E., Leal, M. E., Lloyd, J., Lovett, J. C., Makana, J.-R., Malhi, Y., Marshall, A. R., Ojo, L., Peh, K.
Environ Monit Assess (2017) 189:187 S.-H., Pickavance, G., Poulsen, J. R., Re-itsma, J. M., Sheil, D., Simo, M., Steppe, K., Taedoumg, H. E., Talbot, J., Taplin, J. R. D., Taylor, D., Thomas, S. C., Toirambe, B., Verbeeck, H., Vleminckx, J., White, L. J. T., Willcock, S., Woell, H., & Zemagho, L. (2013). Above-ground biomass and structure of 260 African tropical forests. Philosophical transactions of The Royal Society Botany, 368, 1–17. Madugundu, R., Vyjayanthi, N., & Jha, C. S. (2008). Estimation of LAI and above-ground biomass in deciduous forests: Western Ghats of Karnataka, India. International Journal of Applied Earth Observation and Geoinformation, 10, 211– 219. Malhi, Y., Baldocchi, D. D., & Jarvis, P. G. (1999). The carbon balance of tropical, temperate and boreal forests. Plant, Cell & Environment, 22, 715–740. Malhi, Y., Wood, D., Baker, T. R., Wright, J., Phillips, O. L., Cochrane, T., Meir, P., Chave, J., Almeida, S., Arroyo, L., Higuchi, N., Killeen, T. J., Laurance, S. G., Laurance, W. F., Lewis, S. L., Monteagudo, A., Neill, D. A., Núñez-Vargas, P., Pitman, N. C. A., Quesada, C. A., Salomão, R., Silva, J. N. M., Torres-Lezama, A., Terborgh, J., Vásquez-Martínez, R., & Vinceti, B. (2006). The regional variation in aboveground live biomass in old-growth Amazonian forests. Global Change Biology, 12, 1107–1138. Mani, S., & Parthasarathy, N. (2007). Above-ground biomass estimation in ten tropical dry evergreen forest sites of peninsular India. Biomass and Bioenergy, 31, 284–290. Mani, S., & Parthasarathy, N. (2009). Tree population and aboveground biomass changes in two disturbed tropical dry evergreen forests of peninsular India. Tropical Ecology, 50(2), 249–258. Mohanraj, R., Saravanan, J., & Dhanakumar, S. (2011). Carbon stock in Kolli forests, Eastern Ghats (India) with emphasis on aboveground biomass, litter, woody debris and soils. iForest, 4, 61–65. Murali, K. S., Bhat, D. M., & Ravindranath, N. H. (2005). Biomass estimation equation for tropical deciduous and evergreen forests. International Journal of Agricultural Resources, Governance and Ecology, 4, 81–92. Murphy, P. G., & Lugo, A. E. (1986). Structure and biomass of a subtropical dry forest in Puerto Rico. Biotropica, 18, 89–96. NADP, (2008). Proceedings of The Meeting Conducted For The Formulation of National Agricultural Development Programme (2008–2009 To 2011–2012) In Tiruvannamalai District of Tamil Nadu India. 1–247. Navar, J., Mendez, E., & Dale, V. (2002). Estimating stand biomass in the Tamaulipan thornscrub of northeastern Mexico. Annals of Forest Science, 59, 813–821. Navar-Chaidez, J. (2011). The spatial distribution of aboveground biomass in tropical forests of Mexico. Tropical and Subtropical Agroecosystems, 13, 149–158. NOAA. (2016). National Oceanic & Atmospheric Administration, The NOAA Annual Greenhouse Gas Index (Aggi) Noaa Earth System Research Laboratory, R/GMD, 325 Broadway, Boulder, CO 80305–3328. Paladines, V. C., & Ruiz, R. G. (2016). Floristic composition and structure of a deciduous dry forest from southern Ecuador: diversity and aboveground carbon accumulation. International Journal of Current Research and Academic Review, 4(3), 154–169.
Environ Monit Assess (2017) 189:187 Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., Phillips, O. L., Shvidenko, A., Lewis, S. L., Canadell, J. G., Ciais, P., Jackson, R. B., Pacala, S. W., McGuire, A. D., Piao, S., Rautiainen, A., Sitch, S., & Hayes, D. (2011). A large and persistent carbon sink in the world’s forests. Science, 333, 988–993. Pan, Y., Birdsey, R. A., Phillips, O. L., & Jackson, R. B. (2013). The structure, distribution, and biomass of the world’s forests. Annual Review of Ecology, Evolution, and Systematics, 44, 593–622. Pande, P. K. (2005). Biomass and productivity in some disturbed tropical dry deciduous teak forest of Satpura plateau, Madhya Pradesh. Tropical Ecology, 46, 229–239. Pawar, G. V., Singh, L., Jhariya, M. K., & Sahu, K. P. (2014). Effect of anthropogenic disturbances on biomass and carbon storage potential of a dry tropical forest in India. Journal of Applied and Natural Science, 6(2), 383–392. Poorter, L., van der Sande, M. T., Thompson, J., Arets, E. J. M. M., Alarcón, A., Álvarez-Sánchez, J., Ascarrunz, N., Balvanera, P., Barajas-Guzmán, G., Boit, A., Bongers, F., Carvalho, F. A., Casanoves, F., Cornejo-Tenorio, G., Costa, F. R. C., de Castilho, C. V., Duivenvoorden, J. F., Dutrieux, L. P., Enquist, B. J., Fernández-Méndez, F., Finegan, B., Gormley, L. H. L., Healey, J. R., Hoosbeek, M. R., IbarraManríquez, G., Junqueira, A. B., Levis, C., Licona, J. C., Lisboa, L. S., Magnusson, W. E., Martínez-Ramos, M., Martínez-Yrizar, A., Martorano, L. G., Maskell, L. C., Mazzei, L., Meave, J. A., Mora, F., Muñoz, R., Nytch, C., Pansonato, M. P., Parr, T. W., Paz, H., Pérez-García, E. A., Rentería, L. Y., Rodríguez-Velazquez, J., Rozendaal, D. M. A., Ruschel, A. R., Sakschewski, B., Salgado-Negret, B., Schietti, J., Simões, M., Sinclair, F. L., Souza, P. F., Souza, F. C., Stropp, J., ter Steege, H., Swenson, N. G., Thonicke, K., Toledo, M., Uriarte, M., van der Hout, P., Walker, P., Zamora, N., & Pena-Claros, M. (2015). Diversity enhances carbon storage in tropical forests. Global Ecology and Biogeography, 24(11), 1314–1328. Prado-Junior, J. A., Schiavini, I., Vale, V. S., Arantes, C. S., van der Sande, M. T., Lohbeck, M., & Poorter, L. (2016). Conservative species drive biomass productivity in tropical dry forests. Journal of Ecology, 104, 817–827. Pragasan, L. A. (2015a). Assessment of tree carbon stock in the Kalrayan hills of the Eastern Ghats, India. Walailak Journal of Science and Technology, 12, 659–670. Pragasan, A. L. (2015b). Tree carbon stock assessment from the tropical forests of Bodamalai Hills located in India. Journal of Earth Science & Climatic Change, 6, 314. Quesada, C. A., Phillips, O. L., Schwarz, M., Czimczik, C. I., Baker, T. R., Patino, S., Fyllas, N. M., Hodnett, M. G., Herrera, R., Almeida, S., Dávila, E. A., Arneth, A., Arroyo, L., Chao, K. J., Dezzeo, N., Erwin, T., di Fiore, A., Higuchi, N., Coronado, E. M. H., Jimenez, T., Killeen, A. T., Lezama, G., Lloyd, G., López-González, E., Luizao, F. J., Malhi, Y., Monteagudo, A., Neill, D. A., Núnez, V. P., Paiva, R., Peacock, J., Penuela, M. C., Pena Cruz, A., Pitman, N., Priante Filho, N., Prieto, A., Ramırez, H., Rudas, A., Salomao, R., Santos, A. J. B., Schmerler, J., Silva, N., Silveira, M., Vásquez, R., Vieira, I., Terborgh, J., & Lloyd, J. (2012). Basin-wide variations in Amazon forest structure and function are mediated by both soils and climate. Biogeosciences, 9, 2203–2246.
Page 17 of 18 187 Rai, S. N. (1984). Bole, branch, current year twig, leaf and root biomass production in tropical rain (wet evergreen) forests of Western Ghats of Karnataka. Indian Forester, 110(9), 901–913. Rai, S. N., & Proctor, J. (1986). Ecological studies on four rain forests in Karnataka, India I. Environment, structure, floristics and biomass. Journal of Ecology, 74, 439–454. Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A., Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., Petrova, S., White, L., Silman, M., & Morel, A. (2011). Benchmark map of forest carbon stock in tropical regions across three continents. PNAS early edition, environmental sciences (pp. 1–6). Irvine: University of California. Sahu, S. C., Suresh, H. S., & Ravindranath, N. H. (2016). Forest structure, composition and above ground biomass of tree community in tropical dry forests of Eastern Ghats, India. Notulae Scientia Biologicae, 8(1), 125–133. Salunkhe, O., Khare, P. K., Sahu, T. R., & Singh, S. (2014). Aboveground Biomass and carbon stocking in tropical deciduous forests of state of Madhya Pradesh, India. Taiwania, 59(4), 353–359. Salunkhe, O., Khare, P. K., Sahu, T. R., & Singh, S. (2016). Estimation of tree biomass reserves in tropical deciduous forests of Central India by non-destructive approach. Tropical Ecology, 57(2), 153–161. Schnitzer, S. A., Dewalt, S. J., & Chave, J. (2006). Censusing and measuring lianas: a quantitative comparison of the common methods. Biotropica, 38, 581–591. Sharma, R. (2010). Environmental impact assessment of water resources projects with special reference to Sathanur reservoir project, India. Sharma, C. M., Gairola, S., Baduni, N. P., Ghildiyal, S. K., & Sarvesh, S. (2011). Variation in carbon stocks on different slope aspects in seven major types of temperate region of Garhwal Himalaya, India. Journal of Biosciences, 36, 701–708. Sicard, P., & Dalstein-Richier, L. (2015). Health and vitality assessment of two common pine species in the context of climate change in southern Europe. Environmental Research, 137, 235–245. Singh, L., & Singh, J. S. (1991). Storage and flux of nutrients in a dry tropical forest in India. Annual of Botany, 68, 275–284. Singh, L., & Singh, J. S. (1993). Importance of short-lived components of a dry tropical forest for biomass production and nutrient cycling. Journal of Vegetation Science, 4, 681–686. Singh, L., Yadav, D. K., Pagare, P., Gosh, L., & Thakur, B. S. (2009). Impact of land use changes on species structure, biomass and carbon storage in tropical deciduous forest and converted forest. International Journal of Ecology and Environmental Sciences., 35(1), 113–119. Slik, J. W. F., Aiba, S. I., Brearley, F. Q., Cannon, C. H., Forshed, O., Kitayama, K., Nagamasu, H., Nilus, R., Payne, J., Paoli, G., Poulsen, A. D., Raes, N., Sheil, D., Sidiyasa, K., Suzuki, E., & Valkenburg, J. L. C. H. V. (2010). Environmental correlates of tree biomass, basal area, wood specific gravity and stem density gradients in Borneo’s tropical forests. Global Ecology and Biogeography, 19, 50–60. Slik, J. W. F., Paoli, G., McGuire, K., Amaral, I., Barroso, J., Bastian, M., Blanc, L., Bongers, F., Boundja, P., & Clark, C. (2013). Large trees drive forest aboveground biomass variation in moist lowland forests across the tropics. Global Ecology and Biogeography, 22, 1261–1271.
187
Page 18 of 18
Spracklen, D. V., & Righelato, R. (2016). Carbon storage and sequestration of re-growing montane forests in southern Ecuador. Forest Ecology and Management, 364, 139–144. Dar, J. A., & Sundarapandian, S. M. (2015). Variation of biomass and carbon pools with forest type in temperate forests of Kashmir Himalaya, India. Environmental Monitoring and Assessment, 187, 55. doi:10.1007/s10661-015-4299-7. Sundarapandian, S. M., Dar, J. A., Gandhi, D. S., Srinivas, K. K., & Subashree, K. (2013). Estimation of biomass and carbon stocks in tropical dry forests in Sivagangai district, Tamil Nadu, India. International Journal of Environmental Science and Engineering research, 4(3), 66–76. Sundarapandian, S. M., Mageswaran, K., Sanjay Gandhi, D., & Dar, J. A. (2014a). Impact of thane cyclone on tree damage in Pondicherry University campus, Puducherry, India. Current World Environment, 9(2), 287–300. Sundarapandian, S. M., Amritha, S., Gowsalya, L., Kayathri, P., Thamizharasi, M., Dar, J. A., Srinivas, K., & Gandhi, D. S. (2014b). Biomass and carbon stock assessments of woody vegetation in Pondicherry University campus, Puducherry. International Journal of Environmental Biology, 4(2), 87–99.
Environ Monit Assess (2017) 189:187 Sundarapandian, S. M., Naveenkumar, J. & Arunkumar, K. S. (2016). Diversity and carbon stocks of Liana Community in tropical dry forests of Javadi Hill, Eastern Ghats. National Conference on BClimate Change Impacts and Natural Resource Management^-18th & 19th February 2016 Technical Sessions. 109–117. Tarnocai, C., Canadell, J. G., Schuur, E. A. G., Kuhry, P., Mazhitova, G., & Zimov, S. (2009). Soil organic carbon pools in the northern circumpolar permafrost region. Global Biogeo- chemical Cycles, 23, GB2023. Usuga, J. C., Toro, J. A., Alzate, M. V., & Tapias, A. D. J. L. (2010). Estimation of biomass and carbon stocks in plants, soil and forest floor in different tropical forests. Forest Ecology and Management, 260, 1906–1913. Whitmore, T. C. (1984). Tropical rain forests of the Far East (second ed.). Oxford: Clarendon Press. WHO. (2015). Climate change and human health, Switzerland Wiemann, M. C., & Green, D. W. (2007). Estimating Janka hardness from specific gravity for tropical and temperate species. Research Paper FPL-RP-643. Madison: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory 21p.
