Comparative study of forest biomass and carbon stocks of Margalla Hills National Park, Pakistan

References

• Ackermann N. 2015. Growing stock volume estimation in temperate forested areas using a fusion approach with SAR satellites imagery. Switzerland: Springer International Publishing.
   Google Scholar
• Aftab E, Hickey G. 2010. Forest administration challenges in Pakistan: the case of the Patriata reserved forest and the new Murree development. Inter Forestry Rev. 12(1):97–105.
   Web of Science ®Google Scholar
• Afzal S, Nawaz MF, Siddiqui MT, Aslam Z. 2018. Comparative study on water use efficiency between introduced species (Eucalyptus camaldulensis) and indigenous species (Tamarix aphylla) on marginal sandy lands of Noorpur Thal. PAKJAS. 55(01):127–135.
   Google Scholar
• Akbar S. 2012. A sociological study exploring influence of natural flora on livelihood strategies of rural communities (A Case Study of Margalla Hills). [Master’s Thesis]. Islamabad: International Islamic University.
   Google Scholar
• Ali A, Ashraf I, Gulzar S, Akmal M. 2020a. Development of an allometric model for biomass estimation of Pinus roxburghii, Growing in Subtropical Pine Forests of Khyber Pakhtunkhwa, Pakistan. SJA. 35(1):236–244.
   Google Scholar
• Ali A, Ashraf MI, Gulzar S, Akmal M. 2020b. Estimation of forest carbon stocks in temperate and subtropical mountain systems of Pakistan: implications for REDD+ and climate change mitigation. Environ Monit Assess. 192(3):198.
   PubMed Web of Science ®Google Scholar
• Ali A, Ullah S, Bushra S, Ahmad N, Ali A, Khan MA. 2018. Quantifying forest carbon stocks by integrating satellite images and forest inventory data. Austr J Forest Sci. 135(2):93–117.
   Web of Science ®Google Scholar
• Ali F, Khan N, Ahmad A, Khan AA. 2019. Structure and biomass carbon of Olea ferruginea forests in the foot hills of Malakand division, Hindukush range mountains of Pakistan. Acta Ecologica Sinica. 39(4):261–266.
   Google Scholar
• Ali N, Hu X, Hussain J. 2020. The dependency of rural livelihood on forest resources in Northern Pakistan’s Chaprote Valley. Global Ecol Conserv. 22:e01001.
   Web of Science ®Google Scholar
• Amir M, Liu X, Ahmad A, Saeed S, Mannan A, Muneer MA. 2018. Patterns of biomass and carbon allocation across chronosequence of Chir Pine (Pinus roxburghii) forest in Pakistan: inventory-based estimate. Adv Meteorol. 2018:e3095891–8.
   Web of Science ®Google Scholar
• Anwar M, Chapman J. 2000. Feeding habits and food of grey goral in the Margalla hills national park [Pakistan]. Pakistan J Agri Res. 16:28–32.
   Google Scholar
• Anwar T, Ilyas N, Qureshi R, Munazir M, Rahim BZ, Qureshi H, Kousar R, Maqsood M, Abbas Q, Bhatti MI, et al. 2019. Allelopathic potential of Pinus Roxburghii needles against selected weeds of wheat crop. Appl Ecol Env Res. 17(2):1717–1739.
   Web of Science ®Google Scholar
• Arif M, Shahzad M, Elzaki E, Hussain A, Zhang B, Yukun C. 2017. Biomass and carbon stocks estimation in Chichawatni irrigated plantation in Pakistan. Inter J Agric Biol. 19:1339–1349.
   Web of Science ®Google Scholar
• Aryal S, Bhuju D, Kharal D, Gaire N, Dyola N. 2018. Climatic upshot using growth pattern of Pinus roxburghii from western Nepal. Pak J Bot. 50(2):579–588.
   Web of Science ®Google Scholar
• Asrat Z, Eid T, Gobakken T, Negash M. 2020. Aboveground tree biomass prediction options for the Dry Afromontane forests in south-central Ethiopia. For Ecol Manage. 473(1):118335.
   Google Scholar
• Ballabha R, Tiwari J, Tiwari P. 2013. Regeneration of tree species in the sub-tropical forest of Alaknanda Valley, Garhwal Himalaya, India. For Sci Pract. 15(2):89–97.
   Google Scholar
• Banday M, Bhardwaj DR, Pala NA. 2018. Variation of stem density and vegetation carbon pool in subtropical forests of Northwestern Himalaya. J Sustainable For. 37(4):389–402.
   Google Scholar
• Best DJ, Roberts DE. 1975. Algorithm AS 89: the upper tail probabilities of spearman’s Rho. J R Stat Soc Series C. 24(3):377–379.
   Google Scholar
• Bhadwal S, Singh R. 2002. Carbon sequestration estimates for forestry options under different land-use scenarios in India. Curr Sci. 83(11):1380–1386.
   Web of Science ®Google Scholar
• Bilal H, Nisa S, Ali SS. 2014. Effects of exotic eucalyptus plantation on the ground and surface water of district Malakand, Pakistan. Inter J Innov Sci Res. 8(2):299–304.
   Google Scholar
• Bin Y, Ye W, Muller-Landau HC, Wu L, Lian J, Cao H. 2012. Unimodal tree size distributions possibly result from relatively strong conservatism in intermediate size classes. PLoS One. 7(12):e52596.
   PubMed Web of Science ®Google Scholar
• Bojinski S, Verstraete M, Peterson TC, Richter C, Simmons A, Zemp M. 2014. The concept of essential climate variables in support of climate research, applications, and policy. Bull Am Meteorol Soc. 95(9):1431–1443.
   Web of Science ®Google Scholar
• Brown SL, Schroeder P, Kern JS. 1999. Spatial distribution of biomass in forests of the eastern USA. For Ecol Manage. 123(1):81–90.
   Web of Science ®Google Scholar
• Buchholz T, Friedland AJ, Hornig CE, Keeton WS, Zanchi G, Nunery J. 2014. Mineral soil carbon fluxes in forests and implications for carbon balance assessments. GCB Bioenergy. 6(4):305–311.
   Google Scholar
• Butt A, Shabbir R, Ahmad SS, Aziz N, Nawaz M, Shah MTA. 2015. Land cover classification and change detection analysis of Rawal watershed using remote sensing data. J Bio & Env Sci. 6(1):236–248.
   Google Scholar
• Cairns MA, Brown S, Helmer EH, Baumgardner GA. 1997. Root biomass allocation in the world’s upland forests. Oecologia. 111(1):1–11.
   PubMed Web of Science ®Google Scholar
• Campbell BM. 2009. Beyond Copenhagen: REDD plus, agriculture, adaptation strategies and poverty. Global Environ Change. 19(4):397–399.
   Web of Science ®Google Scholar
• Campbell DG, Stone JL, Rosas A. 1992. A comparison of the phytosociology and dynamics of three floodplain (Várzea) forests of known ages, Rio Juruá, western Brazilian Amazon. Botan J Linnean Soc. 108(3):213–237.
   Web of Science ®Google Scholar
• Chaturvedi OP, Singh JS. 1987. The structure and function of pine forest in Central Himalaya. I. Dry matter dynamics. Ann Bot. 60(3):237–252.
   Web of Science ®Google Scholar
• Chaudhary LB, Sudhakar JV, Kumar A, Bajpai O, Tiwari R, Murthy VS. 2012. Synopsis of the Genus Ficus L. (Moraceae) in India. Taiwania. 57(2):193–216.
   Google Scholar
• Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus D, Fölster H, Fromard F, Higuchi N, Kira T, et al. 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia. 145(1):87–99.
   PubMed Web of Science ®Google Scholar
• Chave J, Condit R, Aguilar S, Hernandez A, Lao S, Perez R. 2004. Error propagation and scaling for tropical forest biomass estimates. Philos Trans R Soc Lond B Biol Sci. 359(1443):409–420.
   PubMed Web of Science ®Google Scholar
• Chave J, Réjou-Méchain M, Búrquez A, Chidumayo E, Colgan MS, Delitti WBC, Duque A, Eid T, Fearnside PM, Goodman RC, et al. 2014. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Chang Biol. 20(10):3177–3190.
   PubMed Web of Science ®Google Scholar
• Chin W. 1998. The partial least squares approach to structural equation modeling. In: Marcoulides GA, editor. Modern methods for business research. New Jersey, London: Lawrence Erlbaum Associates. p. 295–358.
   Google Scholar
• Clark DA, Brown S, Kicklighter DW, Chambers JQ, Thomlinson JR, Ni J, Holland EA. 2001. Net primary production in tropical forests: an evaluation and synthesis of existing field data. Ecol Appl. 11(2):371–384.
   Web of Science ®Google Scholar
• Debastiani AB, Sanquetta CR, Corte APD, Pinto NS, Rex FE. 2019. Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest. Ann for Res. 52(1):109–122.
   Google Scholar
• Devagiri GM, Money S, Singh S, Dadhawal VK, Patil P, Khaple A, Devakumar AS, Hubballi S. 2013. Assessment of above ground biomass and carbon pool in different vegetation types of south western part of Karnataka, India using spectral modeling. Trop Ecol. 54(2):149–165.
   Web of Science ®Google Scholar
• Dimobe K, Kuyah S, Dabré Z, Ouédraogo A, Thiombiano A. 2019. Diversity-carbon stock relationship across vegetation types in W National park in Burkina Faso. For Ecol Manage. 438:243–254.
   Web of Science ®Google Scholar
• Dlugokencky E, Tans P. 2020. Trends in atmospheric carbon dioxide, National Oceanic and Atmospheric Administration, Earth System Research Laboratory (NOAA/ESRL). https://www.esrl.noaa.gov/gmd/ccgg/trends/global.html
   Google Scholar
• Dumitrașcu M, Kucsicsa G, Dumitrică C, Popovici E-A, Vrînceanu A, Mitrică B, Mocanu I, Șerban P-R. 2020. Estimation of future changes in aboveground forest carbon stock in Romania. A prediction based on forest-cover pattern scenario. Forests. 11(9):914.
   Web of Science ®Google Scholar
• FAO 2010. Global forest resources assessment 2010: main Report. Rome, Italy: Food and Agriculture Organization of the United Nations (FAO).
   Google Scholar
• FAO 2020a. Global forest resource assessment 2020. Terms and definitions. Rome, Italy: FAO.
   Google Scholar
• FAO 2020b. Global forest resource assessment 2020: main report. Rome, Italy: Food and Agriculture Organization of the United Nations (FAO).
   Google Scholar
• Fawzy S, Osman AI, Doran J, Rooney DW. 2020. Strategies for mitigation of climate change: a review. Environ Chem Lett. 18(6):2069–2094.
   Web of Science ®Google Scholar
• Feldpausch TR, Jirka S, Passos CAM, Jasper F, Riha SJ. 2005. When big trees fall: damage and carbon export by reduced impact logging in southern Amazonia. For Ecol Manage. 219(2-3):199–215.
   Web of Science ®Google Scholar
• Feldpausch TR, Lloyd J, Lewis SL, Brienen RJW, Gloor M, Monteagudo Mendoza A, Lopez-Gonzalez G, Banin L, Abu Salim K, Affum-Baffoe K, et al. 2012. Tree height integrated into pantropical forest biomass estimates. Biogeosciences. 9(8):3381–3403.
   Web of Science ®Google Scholar
• FRI 2018. Impact of ban on green felling on biophysical status of forest. Dehradun, India: Forest Research Institute (FRI).
   Google Scholar
• Gandhi DS, Sundarapandian S. 2017. Large-scale carbon stock assessment of woody vegetation in tropical dry deciduous forest of Sathanur reserve forest, Eastern Ghats, India. Environ Monit Assess. 189(4):187.
   PubMed Web of Science ®Google Scholar
• Ghimire P. 2019. Carbon Sequestration Potentiality of Pinus roxburghii Forest in Makawanpur District of Nepal. JEECE. 4(1):7.
   Google Scholar
• Giday K, Eshete G, Barklund P, Aertsen W, Muys B. 2013. Wood biomass functions for Acacia abyssinica trees and shrubs and implications for provision of ecosystem services in a community managed exclosure in Tigray, Ethiopia. J Arid Environ. 94:80–86.
   Web of Science ®Google Scholar
• Gogoi RR, Adhikari D, Upadhaya K, Barik SK. 2020. Tree diversity and carbon stock in a subtropical broadleaved forest are greater than a subtropical pine forest occurring in similar elevation of Meghalaya, north-eastern India. Trop Ecol. 61(1):142–149.
   Web of Science ®Google Scholar
• Golub AA, Fuss S, Lubowski R, Hiller J, Khabarov N, Koch N, Krasovskii A, Kraxner F, Laing T, Obersteiner M, et al. 2018. Escaping the climate policy uncertainty trap: options contracts for REDD+. Climate Policy. 18(10):1227–1234.
   Web of Science ®Google Scholar
• Goodman RC, Phillips OL, Baker TR. 2014. The importance of crown dimensions to improve tropical tree biomass estimates. Ecol Appl. 24(4):680–698.
   PubMed Web of Science ®Google Scholar
• Gray HR. 1956. The form and taper of forest-tree stems. United Kingdom: Imperial Forestry Institute, University of Oxford.
   Google Scholar
• Gupta B, Dass B. 2007. Composition of herbage in Pinus roxburghii Sargent stands: basal area and importance value index. Casp J Environ Sci. 5(2):93–98.
   Google Scholar
• Haripriya GS. 2000. Estimates of biomass in Indian forests. Biomass Bioenergy. 19(4):245–258.
   Web of Science ®Google Scholar
• Hasen-Yusuf M, Treydte AC, Abule E, Sauerborn J. 2013. Predicting aboveground biomass of woody encroacher species in semi-arid rangelands, Ethiopia. J Arid Environ. 96:64–72.
   Web of Science ®Google Scholar
• Himalayan Wildlife Foundation 2007. Margallah Hills National Park Ecological Baseline. Islamabad, Pakistan: Himalayan Wildlife Foundation and Capital Development Authority.
   Google Scholar
• Hossain M, Siddique MRH, Abdullah SMR, Saha C, Islam SMZ, Iqbal MZ, Akhter M. 2020. Development and evaluation of species-specific biomass models for most common timber and fuelwood species of Bangladesh. OJF. 10(01):172–185.
   Google Scholar
• Hussain J, Zhou K, Akbar M, Khan MZ, Raza G, Ali S, Hussain A, Abbas Q, Khan G, Khan M, et al. 2019. Dependence of rural livelihoods on forest resources in Naltar Valley, a dry temperate mountainous region, Pakistan. Global Ecol Conserv. 20:e00765.
   Web of Science ®Google Scholar
• Hussain M, Butt AR, Uzma F, Ahmed R, Rehman A, Ali MU, Ullah H, Yousaf B. 2019. Divisional disparities on climate change adaptation and mitigation in Punjab, Pakistan: local perceptions, vulnerabilities, and policy implications. Environ Sci Pollut Res Int. 26(30):31491–31507.
   PubMed Web of Science ®Google Scholar
• Iles K, Wilson LJ. 1977. A further neglected mean. MT. 70(1):27–28.
   Google Scholar
• IPCC 2007. Climate change 2007: the physical science basis. contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. (Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL, editors). Cambridge, UK and New York, USA: Cambridge University Press.
   Google Scholar
• IPCC 2019. Technical summary, 2019. In: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. (Shukla PR, Skea J, Slade R, van Diemen R, Haughey E, Malley J, Pathak M, Pereira JP, editors). Cambridge: Cambridge University Press.
   Google Scholar
• IPCC 2023. Synthesis report of the IPCC sixth assessment report (AR6). Summary for policymakers. Switzerland: IPCC.
   Google Scholar
• Ismail I, Sohail M, Gilani H, Ali A, Hussain K, Hussain K, Karky BS, Qamer FM, Qazi W, Ning W, et al. 2018. Forest inventory and analysis in Gilgit-Baltistan: a contribution towards developing a forest inventory for all Pakistan. IJCCSM. 10(4):616–631.
   Google Scholar
• Jackson JK. 1994. Manual of afforestation in Nepal. Kathmandu, Nepal: Forest Research and Survey Centre, Ministry of Forests and Soil Conservation.
   Google Scholar
• Joos F, Spahni R. 2008. Rates of change in natural and anthropogenic radiative forcing over the past 20,000 years. Proc Natl Acad Sci USA. 105(5):1425–1430.
   PubMed Web of Science ®Google Scholar
• Joshi NK, Dhiman RC. 1992. Lopping yield studies of Grewia optiva Drummond. Van Vigyan. 30(2):80–85.
   Google Scholar
• Kanwar A. 2019. Allometric estimation of biomass and carbon sequestration potential of some important agroforestry tree species of mid-hills of Himachal Pradesh. [M.Sc Thesis]. Himachal Pradesh, India: UHF, NAUNI.
   Google Scholar
• Kassambara A. 2023. ggpubr: “ggplot2” Based Publication Ready Plots. https://CRAN.R-project.org/package=ggpubr
   Google Scholar
• Kaushal S, Baishya R. 2021. Stand structure and species diversity regulate biomass carbon stock under major Central Himalayan forest types of India. Ecol Process. 10(1):14.
   Web of Science ®Google Scholar
• Khan A, Ahmed M, Ahmed F, Saeed R, Siddiqui F. 2020. Vegetation of highly disturbed conifer forests around Murree, Pakistan. Türk Biyoçeşitlilik Dergisi. 3(2):43–53.
   Google Scholar
• Khan I, Lei H, Shah IA, Ali I, Khan I, Muhammad I, Huo X, Javed T. 2020. Farm households’ risk perception, attitude and adaptation strategies in dealing with climate change: promise and perils from rural Pakistan. Land Use Policy. 91:104395.
   Web of Science ®Google Scholar
• Khan M, Mahmood HZ, Abbas G, Damalas CA. 2017. Agroforestry systems as alternative land-use options in the Arid Zone of Thal, Pakistan. Small-Scale Forestry. 16(4):553–569.
   Web of Science ®Google Scholar
• Khan MS, Khan S, Shah W, Hussain A, Masaud S. 2016. Height growth, diameter increment and age relationship response to sustainable volume of subtropical Chir pine (Pinus roxburghii) forest of Karaker Barikot forest Muhammad Sadiq khan. PAB. 5(4):760–767.
   Google Scholar
• Khan N, Ali K, Shaukat S. 2014. Phytosociology, structure and dynamics of Pinus roxburghii associations from Northern Pakistan. J for Res. 25(3):511–521.
   Google Scholar
• Kindermann GE, Obersteiner M, Rametsteiner E, McCallum I. 2006. Predicting the deforestation-trend under different carbon-prices. Carbon Balance Manage. 1(1):15.
   PubMedGoogle Scholar
• Koch B. 2010. Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS J Photogramm Remote Sens. 65(6):581–590.
   Web of Science ®Google Scholar
• Köhl M, Ehrhart H-P, Knauf M, Neupane PR. 2020. A viable indicator approach for assessing sustainable forest management in terms of carbon emissions and removals. Ecol Indic. 111:106057.
   Web of Science ®Google Scholar
• Köhl M, Magnussen S, Marchetti M. 2006. Sampling methods, remote sensing and GIS multiresource forest inventory. Berlin, London: springer.
   Google Scholar
• Köhl M, Neupane PR, Lotfiomran N. 2017. The impact of tree age on biomass growth and carbon accumulation capacity: a retrospective analysis using tree ring data of three tropical tree species grown in natural forests of Suriname. PLoS One. 12(8):e0181187.
   PubMed Web of Science ®Google Scholar
• Köhl M, Neupane PR, Mundhenk P. 2020. REDD + measurement, reporting and verification–A cost trap? Implications for financing REDD + MRV costs by result-based payments. Ecol Econ. 168:106513.
   Web of Science ®Google Scholar
• Kumar A, Sharma MP. 2015. Estimation of carbon stocks of Balganga Reserved Forest, Uttarakhand, India. Forest Sci Technol. 11(4):177–181.
   Web of Science ®Google Scholar
• Kumar L, Mutanga O. 2017. Remote sensing of above-ground biomass. Remote Sensing. 9(9):935.
   Web of Science ®Google Scholar
• Kumar M, Anemsh K, Sheikh M, Raj AJ. 2012. Structure and carbon stock potential in traditional agro forestry system of Garhwal Himalaya. Inter J Agric Technol. 8(7):2187–2200.
   Google Scholar
• Kumar M, Kumar R, Konsam B, Sheikh M, Pandey R. 2019. Above-and below-ground biomass production in Pinus roxburghii forests along altitudes in Garhwal Himalaya, India. Current Science. 116(9):1506.
   Web of Science ®Google Scholar
• Kunwar RM, Sharma SP. 1970. Quantitative analysis of tree species in two community forests of Dolpa district, mid-west Nepal. Himal J Sci. 2(3):23–28.
   Google Scholar
• Laar A v, Akça A. 2007. Forest mensuration. 2 ed., Completely rev. and Supplemented. Dordrecht: Springer.
   Google Scholar
• Lugo AE, Brown S. 1992. Tropical forests as sinks of atmospheric carbon. For Ecol Manage. 54(1-4):239–255.
   Web of Science ®Google Scholar
• Lutz JA, Furniss TJ, Johnson DJ, Davies SJ, Allen D, Alonso A, Anderson-Teixeira KJ, Andrade A, Baltzer J, Becker KML, et al. 2018. Global importance of large-diameter trees. Global Ecol Biogeogr. 27(7):849–864.
   Google Scholar
• Macías CAS, Orihuela JCA, Abad SI. 2017. Estimation of above-ground live biomass and carbon stocks in different plant formations and in the soil of dry forests of the Ecuadorian coast. Food Energy Secur. 6(4):e00115.
   Web of Science ®Google Scholar
• Mandal RA, Dutta IC, Jha PK. 2017. Comparing carbon stock and increment estimation using destructive sampling and inventory guideline of Acacia Catechu, Dalbergia Sissoo, Eucalyptus Camaldulensis and Pylunthus emblica Plantations, Mahottary Nepal. Inter J Chem Mathem Phys. 1(1):48–54.
   Google Scholar
• Mannan A, Feng Z, Ahmad A, Liu J, Saeed S, Mukete B. 2018. Carbon dynamic shifts with land use change in Margallah hills national park, Islamabad (Pakistan) from 1990 to 2017. Appl Ecol Env Res. 16(3):3197–3214.
   Web of Science ®Google Scholar
• Mannan A, Liu J, Zhongke F, Khan TU, Saeed S, Mukete B, ChaoYong S, Yongxiang F, Ahmad A, Amir M, et al. 2019. Application of land-use/land cover changes in monitoring and projecting forest biomass carbon loss in Pakistan. Global Ecol Conserv. 17:e00535.
   Web of Science ®Google Scholar
• Maraseni TN, Neupane PR, Lopez-Casero F, Cadman T. 2014. An assessment of the impacts of the REDD + pilot project on community forests user groups (CFUGs) and their community forests in Nepal. J Environ Manage. 136(1):37–46.
   PubMedGoogle Scholar
• McElhinny C, Gibbons P, Brack C, Bauhus J. 2005. Forest and woodland stand structural complexity: its definition and measurement. For Ecol Manage. 218(1-3):1–24.
   Web of Science ®Google Scholar
• Mensah S, Noulèkoun F, Ago EE. 2020. Aboveground tree carbon stocks in West African semi-arid ecosystems: dominance patterns, size class allocation and structural drivers. Global Ecol Conserv. 24:e01331.
   Web of Science ®Google Scholar
• Moussa S, Kyereh B, Tougiani A, Kuyah S, Saadou M. 2019. West African Sahelian cities as source of carbon stocks: evidence from Niger. Sustain Cities Soc. 50:101653.
   Web of Science ®Google Scholar
• Nath S, Nath AJ, Sileshi GW, Das AK. 2017. Biomass stocks and carbon storage in Barringtonia acutangula floodplain forests in North East India. Biomass Bioenergy. 98:37–42.
   Web of Science ®Google Scholar
• Naveenkumar J, Arunkumar KS, Sundarapandian S. 2017. Biomass and carbon stocks of a tropical dry forest of the Javadi Hills, Eastern Ghats, India. Carbon Manage. 8(5-6):351–361.
   Web of Science ®Google Scholar
• Negi JDS, Manhas RK, Chauhan PS. 2003. Carbon allocation in different components of some tree species of India: a new approach for carbon estimation. Current Science. 85(11):1528–1531.
   Web of Science ®Google Scholar
• Nero BF, Callo-Concha D, Denich M. 2018. Structure, Diversity, and Carbon Stocks of the Tree Community of Kumasi, Ghana. Forests. 9(9):519.
   Web of Science ®Google Scholar
• Nizami SM. 2010. Estimation of carbon stocks in subtropical managed and unmanaged forests of Pakistan. [PhD Thesis]. Rawalpindi, Pakistan: Arid Agriculture University Rawalpindi Pakistan.
   Google Scholar
• Nizami SM. 2012. The inventory of the carbon stocks in sub tropical forests of Pakistan for reporting under Kyoto Protocol. J for Res. 23(3):377–384.
   Google Scholar
• Nizami SM, Mirza SN, Livesley S, Arndt S, Fox JC, Khan IA, Mahmood T. 2009. Estimating carbon stocks in sub-tropical pine (Pinus roxburghii). Pak J Agri Sci. 46(4):266–270.
   Google Scholar
• Omar H, Misman MA, Kassim AR. 2017. Synergetic of PALSAR-2 and Sentinel-1A SAR polarimetry for retrieving aboveground biomass in dipterocarp forest of Malaysia. Appl Sci. 7(7):675.
   Google Scholar
• Pandey PC, Srivastava PK, Chetri T, Choudhary BK, Kumar P. 2019. Forest biomass estimation using remote sensing and field inventory: a case study of Tripura, India. Environ Monit Assess. 191(9):593.
   PubMed Web of Science ®Google Scholar
• Pant H, Tewari A. 2014. Carbon sequestration in Chir-Pine (Pinus roxburghii Sarg.) forests under various disturbance levels in Kumaun Central Himalaya. J for Res. 25(2):401–405.
   Google Scholar
• Pariyar S, Volkova L, Sharma RP, Sunam R, Weston CJ. 2019. Aboveground carbon of community-managed Chirpine (Pinus roxburghii Sarg.) forests of Nepal based on stand types and geographic aspects. PeerJ. 7:e6494.
   PubMed Web of Science ®Google Scholar
• Pearson T, Walker S, Brown S. 2005. Sourcebook for land use, land-use change and forestry projects. Virginia: Winrock International.
   Google Scholar
• Peng C, Zhang L, Liu J. 2001. Developing and Validating nonlinear height–diameter models for major tree species of ontario’s boreal forests. Northern J Appl Forestry. 18(3):87–94.
   Google Scholar
• Philip MS. 1994. Measuring trees and forests [Internet]. 2nd ed. Wallingford: CAB International.
   Google Scholar
• Polunin O, Stainton A. 1984. Flowers of the Himalaya. Delhi, India: Oxford University Press.
   Google Scholar
• Pommerening A. 2002. Approaches to quantifying forest structures. Forestry: an International J Forest Res. 75(3):305–324.
   Google Scholar
• Portela MGT, de Espindola GM, Valladares GS, Amorim JVA, Frota JCO. 2020. Vegetation biomass and carbon stocks in the Parnaíba River Delta, NE Brazil. Wetlands Ecol Manage. 28(4):607–622.
   Web of Science ®Google Scholar
• Poškus MS, Žukauskienė R. 2017. Predicting adolescents’ recycling behavior among different big five personality types. J Environ Psychol. 54:57–64.
   Web of Science ®Google Scholar
• R Core Team 2023. R: a language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
   Google Scholar
• Raihan A, Begum RA, Mohd Said MN, Abdullah SMS. 2019. A review of emission reduction potential and cost savings through forest carbon sequestration. AJW. 16(3):1–7.
   Google Scholar
• Raihan A, Begum RA, Mohd Said MN, Pereira JJ. 2021. Assessment of carbon stock in forest biomass and emission reduction potential in Malaysia. Forests. 12(10):1294.
   Web of Science ®Google Scholar
• Raihan A, Said MNM. 2022. Cost–Benefit analysis of climate change mitigation measures in the forestry sector of Peninsular Malaysia. Earth Syst Environ. 6(2):405–419.
   Google Scholar
• Rana K, Kumar M, Kumar A. 2020. Assessment of annual shoot biomass and carbon storage potential of Grewia optiva: an approach to combat climate change in Garhwal Himalaya. Water Air Soil Pollut. 231(9):450.
   Web of Science ®Google Scholar
• Ratner B. 2009. The correlation coefficient: its values range between +1/−1, or do they? J Target Meas Anal Mark. 17(2):139–142.
   Google Scholar
• Reichstein M, Bahn M, Ciais P, Frank D, Mahecha MD, Seneviratne SI, Zscheischler J, Beer C, Buchmann N, Frank DC, et al. 2013. Climate extremes and the carbon cycle. Nature. 500(7462):287–295.
   PubMed Web of Science ®Google Scholar
• Reyes-Palomeque G, Dupuy JM, Portillo-Quintero CA, Andrade JL, Tun-Dzul FJ, Hernández-Stefanoni JL. 2021. Mapping forest age and characterizing vegetation structure and species composition in tropical dry forests. Ecol Indic. 120:106955.
   Web of Science ®Google Scholar
• Rodríguez-Veiga P, Wheeler J, Louis V, Tansey K, Balzter H. 2017. Quantifying Forest Biomass Carbon Stocks From Space. Curr Forestry Rep. 3(1):1–18.
   Web of Science ®Google Scholar
• Rosenfield MF, Souza AF. 2013. Biomass and carbon in subtropical forests: overview of determinants, quantification methods and estimates. Neotrop Biol Conserv. 8(2):103–110.
   Google Scholar
• Rossi S, Deslauriers A, Anfodillo T, Carraro V. 2007. Evidence of threshold temperatures for xylogenesis in conifers at high altitudes. Oecologia. 152(1):1–12.
   PubMed Web of Science ®Google Scholar
• Sajid MJ. 2020. Inter-sectoral carbon ties and final demand in a high climate risk country: the case of Pakistan. J Cleaner Prod. 269:122254.
   Web of Science ®Google Scholar
• Salehi B, Mishra AP, Nigam M, Karazhan N, Shukla I, Kiełtyka‐Dadasiewicz A, Sawicka B, Głowacka A, Abu‐Darwish MS, Tarawneh AH, et al. 2021. Ficus plants: state of the art from a phytochemical, pharmacological, and toxicological perspective. Phytother Res. 35(3):1187–1217.
   PubMed Web of Science ®Google Scholar
• Saqib Z, Mahmood A, Naseem Malik R, Mahmood A, Hussian Syed J, Ahmad T. 2014. Indigenous knowledge of medicinal plants in Kotli Sattian, Rawalpindi district, Pakistan. J Ethnopharmacol. 151(2):820–828.
   PubMed Web of Science ®Google Scholar
• Schepaschenko D, Moltchanova E, Shvidenko A, Blyshchyk V, Dmitriev E, Martynenko O, See L, Kraxner F. 2018. Improved estimates of biomass expansion factors for russian forests. Forests. 9(6):312.
   Web of Science ®Google Scholar
• Segura M, Kanninen M. 2005. Allometric models for tree volume and total aboveground biomass in a tropical humid forest in costa rica1. Biotropica. 37(1):2–8.
   Web of Science ®Google Scholar
• Shah H, Bakhsh MI, Amjad M. 1991. Tree growth on farmlands of NWFP. Pak J Forestry. 41(2):74–81.
   Google Scholar
• Shahbaz B, Ali T, Suleri AQ. 2007. A critical analysis of forest policies of Pakistan: implications for sustainable livelihoods. Mitig Adapt Strat Glob Change. 12(4):441–453.
   Google Scholar
• Shaheen H, Khan RWA, Hussain K, Ullah TS, Nasir M, Mehmood A. 2016. Carbon stocks assessment in subtropical forest types of Kashmir Himalayas. Pak J Bot. 48(6):2351–2357.
   Web of Science ®Google Scholar
• Sharma CM, Baduni NP, Gairola S, Ghildiyal SK, Suyal S. 2010. Tree diversity and carbon stocks of some major forest types of Garhwal Himalaya, India. For Ecol Manage. 260(12):2170–2179.
   Web of Science ®Google Scholar
• Sharma K, Bhatta S, Khatri G, Pajiyar A, Joshi D. 2020. Estimation of Carbon Stock in the Chir Pine (Pinus roxburghii Sarg.) Plantation, Forest of Kathmandu Valley, Central Nepal. J Forest Environ Sci. 36(1):37–46.
   Google Scholar
• Sharma RK, Sankhayan PL, Hofstad O. 2008. Forest biomass density, utilization and production dynamics in a western Himalayan watershed. J Res. 19(3):171–180.
   Google Scholar
• Sheikh MI. 1993. Trees of pakistan. Islamabad: GOP-USAID Forestry Planning and Development Project.
   Google Scholar
• Shimano K. 2000. A power function for forest structure and regeneration pattern of pioneer and climax species in patch mosaic forests. Plant Ecology. 146(2):205–218.
   Web of Science ®Google Scholar
• Singh JS, Tiwari AK, Saxena AK. 1985. Himalayan forests: a Net source of carbon for the atmosphere. Envir Conserv. 12(1):67–69.
   Web of Science ®Google Scholar
• Soares MLG, Schaeffer-Novelli Y. 2005. Above-ground biomass of mangrove species. I. Analysis of models. Estuarine Coastal Shelf Sci. 65(1-2):1–18.
   Web of Science ®Google Scholar
• Solomon N, Birhane E, Tadesse T, Treydte AC, Meles K. 2017. Carbon stocks and sequestration potential of dry forests under community management in Tigray, Ethiopia. Ecol Process. 6(1):20.
   Google Scholar
• Soutter ARB, Mõttus R. 2020. “Global warming” versus “climate change”: a replication on the association between political self-identification, question wording, and environmental beliefs. J Environ Psychol. 69:101413.
   Web of Science ®Google Scholar
• Subedi CK, Gurung J, Ghimire SK, Chettri N, Pasakhala B, Bhandari P, Chaudhary RP. 2018. Variation in structure and composition of two pine forests in Kailash Sacred Landscape, Nepal. Banko. 28(1):26–36.
   Google Scholar
• Torres B, Vasseur L, López R, Lozano P, García Y, Arteaga Y, Bravo C, Barba C, García A. 2020. Structure and above ground biomass along an elevation small-scale gradient: case study in an Evergreen Andean Amazon forest, Ecuador. Agroforest Syst. 94(4):1235–1245.
   Web of Science ®Google Scholar
• Vanclay JK. 1994. Modelling forest growth and yield: applications to mixed tropical forests. Wallingford: CAB International.
   Google Scholar
• Verma A, Kaushal R, Alam NM, Mehta H, Chaturvedi OP, Mandal D, Tomar JMS, Rathore AC, Singh C. 2014. Predictive models for biomass and carbon stocks estimation in Grewiaoptiva on degraded lands in western Himalaya. Agroforest Syst. 88(5):895–905.
   Web of Science ®Google Scholar
• Vetaas OR. 2000. The Effect of environmental factors on the regeneration of Quercus semecarpifolia Sm. in Central Himalaya, Nepal. Plant Ecol. 146(2):137–144.
   Web of Science ®Google Scholar
• Vicharnakorn P, Shrestha RP, Nagai M, Salam AP, Kiratiprayoon S. 2014. Carbon stock assessment using remote sensing and forest inventory data in Savannakhet, Lao PDR. Remote Sensing. 6(6):5452–5479.
   Web of Science ®Google Scholar
• Wangda P, Ohsawa M. 2006. Structure and regeneration dynamics of dominant tree species along altitudinal gradient in a dry valley slopes of the Bhutan Himalaya. For Ecol Manage. 230(1-3):136–150.
   Web of Science ®Google Scholar
• Wani N, Velmurugan A, Dadhwal VK. 2010. Assessment of agricultural crop and soil carbon pools in Madhya Pradesh, India. Trop Ecol. 51(1):11–19.
   Web of Science ®Google Scholar
• West PW. 2009. Tree and Forest Measurement. 2nd ed. Berlin, Heidelberg: Springer Berlin Heidelberg.
   Google Scholar
• Wilkes P, Disney M, Vicari MB, Calders K, Burt A. 2018. Estimating urban above ground biomass with multi-scale LiDAR. Carbon Balance Manage. 13(1):1–20.
   PubMedGoogle Scholar
• WWF-Pakistan 2009. Boundary delineation of Margallah Hills National Park (Pakistan). Islamabad, Pakistan: WWF-Pakistan.
   Google Scholar
• Xi-Liu Y, Qing-Xian G. 2018. Contributions of natural systems and human activity to greenhouse gas emissions. Adv Clim Change Res. 9(4):243–252.
   Web of Science ®Google Scholar
• Xu L, Shi Y, Fang H, Zhou G, Xu X, Zhou Y, Tao J, Ji B, Xu J, Li C, et al. 2018. Vegetation carbon stocks driven by canopy density and forest age in subtropical forest ecosystems. Sci Total Environ. 631–632:619–626.
   PubMed Web of Science ®Google Scholar
• Zhao M, Yang J, Zhao N, Liu Y, Wang Y, Wilson JP, Yue T. 2019. Estimation of China’s forest stand biomass carbon sequestration based on the continuous biomass expansion factor model and seven forest inventories from 1977 to 2013. For Ecol Manage. 448:528–534.
   Web of Science ®Google Scholar
• Zobel BJ, Jett JB. 1995. The importance of wood density (Specific Gravity) and its component parts. In: Genetics of wood production. Berlin, Heidelberg: Springer; p. 78–97.
   Google Scholar