Forest Carbon Sequestration by a Pine (Pinus roxburghii) dominant landscape of Uttarakhand and its climate change implications
DOI:
https://doi.org/10.36808/if/2026/v152i6A/171278Keywords:
Forest carbon stock, Aboveground biomass, Machine learning, Random Forest, Remote sensing, Satellite-derived variables, Vegetation indices.Abstract
This study assesses the forest carbon sequestration in a Chir pine (Pinus roxburghii) dominated landscape of the Almora Forest Division, Uttarakhand, India, and examines its implications for climate change mitigation. Given the spatial heterogeneity of forest ecosystems and the limitations of conventional field-based methods, an integrated approach combining field observations, multi-source satellite data, and machine learning was adopted to generate wall-to-wall estimates of aboveground carbon (AGC). A total of 40 sample plots (0.1 ha each) were established across the study area, where tree-level measurements were used to estimate aboveground biomass (AGB) through volumetric and allometric equations, subsequently converted to carbon stock using a standard factor (0.47). Satellite-derived variables from Sentinel-1 (SAR), Sentinel-2 (optical) and topographic variables derived from SRTM data were utilized as predictors in Random Forest algorithm for modeling AGB/AGC estimation. The results revealed substantial spatial variability in AGC, ranging from 47 to 121 Mg ha-1, with a mean value of 75.21 Mg ha-1. The integration of optical and radar data provided complementary insights into vegetation structure and condition, enhancing model robustness. The findings underscore the significance of Chir pine forests as regional carbon reservoirs despite their relatively lower biomass compared to broadleaf systems. The study demonstrates the effectiveness of machine learning and remote sensing integration for largescale carbon mapping and highlights its potential for supporting climate change mitigation strategies, carbon accounting frameworks, and sustainable forest management in Himalayan ecosystems.
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Haq S.M., Yaqoob U., Calixto E.S., Kumar M., Rahman I.U., Hashem A., Abd_Allah E.F., Alakeel M.A., Alqarawi A.A. and Abdalla M. (2021). Long-Term Impact of Transhumance Pastoralism and Associated Disturbances in High-Altitude Forests of Indian Western Himalaya. Sustainability, 13(22): 12497.
Huete A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3): 295–309.
Jain H., Tyagi K., Paygude A., Kumar P., Singh R.K. and Kumar M. (2021). Allometric Equations for the Estimation of Biomass and Carbon in the Sub-tropical Pine Forests of India. Climate Impacts on Sustainable Natural Resource Management, 89–107.
Joshi S., Garg J.K., Kaur A. and Kumar M. (2021a). Assessment of Wildfire Landslide Risk using Spatial Analytics and Deep Learning Techniques for Rudraprayag Forest Division, Uttarakhand. Indian Forester, 147(9): 824–833.
Joshi V.C., Negi V.S., Bisht D., Sundriyal R.C. and Arya D. (2021b). Tree biomass and carbon stock assessment of subtropical and temperate forests in the Central Himalaya, India. Trees, Forests and People, 6: 100147.
Joshi V.C., Sundriyal R.C., Chandra N. and Arya D. (2024). Unlocking nature's hidden treasure: unveiling forest status, biomass and carbon wealth in the Binsar Wildlife Sanctuary, Uttarakhand for climate change mitigation. Environmental Challenges, 14: 100825.
Kala A.K. and Kumar M. (2021). Role of Geospatial Technologies in natural resource management. Climate Impacts on Sustainable Natural Resource Management, 19–34.
Köhl M., Neupane P.R. and 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.
Kothandaraman S., Dar J.A., Sundarapandian S., Dayanandan S. and Khan M.L. (2020). Ecosystem-level carbon storage and its links to diversity, structural and environmental drivers in tropical forests of Western Ghats, India. Scientific Reports, 10(1): 13444.
Kuhn Max (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 28(5): 1–26.
Kumar M., Bussmann R.W. and Swenson N.G. (2025). Chapter 1 - Plant functional traits: the scientific basis and their significance in studying climate change impacts and ecosystem functioning. In M. Kumar, R. W. Bussmann, & N. G. B. T.-P. F. T. Swenson (Eds.), Plant Biology, sustainability and climate change (pp. 1–16). Elsevier. https://doi.org/https://doi.org/ 10.1016/B978-0-443-13367-1.00002-8
Kumar M., Dhyani S. and Kalra N. (2022a). Forest dynamics and conservation: Science, Innovations and policies. Springer Nature.
Kumar M., Dhyani S. and Kalra N. (2022b). Protecting Forest Structure and Functions for Resilience and Sustainability Concerns in the Changing World. In Forest Dynamics and Conservation: Science, Innovations and Policies (pp. 1–31). Springer.
Kumar M., Kalra N., Singh H., Sharma S., Rawat P.S., Singh R.K., Gupta A.K., Kumar P., and Ravindranath N.H. (2021a). Indicator-based vulnerability assessment of forest ecosystem in the Indian Western Himalayas: An analytical hierarchy process integrated approach. Ecological Indicators, 125: 107568.
Kumar M., Phukon S.N. and Singh H. (2021b). The role of communities in sustainable land and forest management. In Forest Resources Resilience and Conflicts (pp. 305–318). Elsevier.
Kumar M., Phukon S.N., Paygude A.C., Tyagi K. and Singh H. (2021c). Mapping Phenological Functional Types (PhFT) in the Indian Eastern Himalayas using machine learning algorithm in Google Earth Engine. Computers & Geosciences, 104982.
Kumar M., Savita, Singh H., Pandey R., Singh M.P., Ravindranath N.H. and Kalra N. (2019). Assessing vulnerability of forest ecosystem in the Indian Western Himalayan region using trends of net primary productivity. Biodiversity and Conservation, 28(8–9): 2163–2182.
Kumar P., Kumar A., Patil M., Hussain S. and Singh A.N. (2024). Factors influencing tree biomass and carbon stock in the Western Himalayas, India. Frontiers in Forests and Global Change, 6: 1328694.
Lal B. and Lodhiyal L.S. (2015). Vegetation structure, biomass and carbon content in Pinus roxburghii Sarg. Dominant forests of Kumaun Himalaya. Environment & We An International Journal of Science & Technology, 10: 117–124.
Pan Y., Birdsey R.A., Fang J., Houghton R., Kauppi P.E., Kurz W.A. and Hayes D. (2011). A large and persistent carbon sink in the world's forests. Science, 333(6045): 988-993.
Pandey R., Alatalo J., Thapliyal K., Chauhan S., Kelli M.A., Gupta A.K., Jha S.K. and Kumar M. (2018). Climate change vulnerability in urban slum communities: Investigating household adaptation and decision-making capacity in the Indian Himalaya. Ecological Indicators.
Peichl M. and Arain M.A. (2006). Above-and belowground ecosystem biomass and carbon pools in an age-sequence of temperate pine plantation forests. Agricultural and Forest Meteorology, 140(1–4): 51–63.
Pimoli M., Joshi V.C., Arya S., Sundriyal R.C. and Yadava A.K. (2024). Impact of forest management on structure, composition, biomass and carbon stock in Chir-pine (P. roxburghii) forest, Western Himalaya. Environmental Challenges, 16: 100964.
Pokhriyal P., Rehman S., Krishna G.A., Rajiv P. and Manoj K. (2020). Assessing forest cover vulnerability in Uttarakhand, India using analytical hierarchy process. Modeling Earth Systems and Environment. https://doi.org/10.1007/s40808-019-00710-y
Prentice I.C., Farquhar G.D., Fasham M.J.R., Goulden M.L., Heimann M., Jaramillo V.J., Kheshgi H.S., Le Quéré C., Scholes R.J. and Wallace D.W.R. (2001). The carbon cycle and atmospheric carbon dioxide.
R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Rawat A.S., Kalra N., Singh H. and Kumar M. (2020). Application of Vegetation Models in India for Understanding the Forest Ecosystem Processes. Indian Forester, 146(2): 99–100.
Savita K.M. and Kushwaha S.P.S. (2018). Forest Resource Dependence and Ecological Assessment of forest Fringes in Rainfed Districts of India. Indian Forester, 144(3): 211–220.
Singh H. and Kumar M. (2022). Climate Change and Its Impact on Indian Himalayan Forests: Current Status and Research Needs. In Climate Change: Impacts, Responses and Sustainability in the Indian Himalaya (pp. 223–242). Springer.
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